ereniko commited on
Commit
f1c1983
·
verified ·
1 Parent(s): aea26af

Upload folder using huggingface_hub

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. README.md +62 -3
  2. adapter_config.json +43 -0
  3. adapter_model.safetensors +3 -0
  4. chat_template.jinja +6 -0
  5. checkpoint-1000/README.md +209 -0
  6. checkpoint-1000/adapter_config.json +43 -0
  7. checkpoint-1000/adapter_model.safetensors +3 -0
  8. checkpoint-1000/chat_template.jinja +6 -0
  9. checkpoint-1000/optimizer.pt +3 -0
  10. checkpoint-1000/rng_state.pth +3 -0
  11. checkpoint-1000/scaler.pt +3 -0
  12. checkpoint-1000/scheduler.pt +3 -0
  13. checkpoint-1000/tokenizer.json +0 -0
  14. checkpoint-1000/tokenizer_config.json +17 -0
  15. checkpoint-1000/trainer_state.json +1034 -0
  16. checkpoint-1000/training_args.bin +3 -0
  17. checkpoint-1500/README.md +209 -0
  18. checkpoint-1500/adapter_config.json +43 -0
  19. checkpoint-1500/adapter_model.safetensors +3 -0
  20. checkpoint-1500/chat_template.jinja +6 -0
  21. checkpoint-1500/optimizer.pt +3 -0
  22. checkpoint-1500/rng_state.pth +3 -0
  23. checkpoint-1500/scaler.pt +3 -0
  24. checkpoint-1500/scheduler.pt +3 -0
  25. checkpoint-1500/tokenizer.json +0 -0
  26. checkpoint-1500/tokenizer_config.json +17 -0
  27. checkpoint-1500/trainer_state.json +1534 -0
  28. checkpoint-1500/training_args.bin +3 -0
  29. checkpoint-2000/README.md +209 -0
  30. checkpoint-2000/adapter_config.json +43 -0
  31. checkpoint-2000/adapter_model.safetensors +3 -0
  32. checkpoint-2000/chat_template.jinja +6 -0
  33. checkpoint-2000/optimizer.pt +3 -0
  34. checkpoint-2000/rng_state.pth +3 -0
  35. checkpoint-2000/scaler.pt +3 -0
  36. checkpoint-2000/scheduler.pt +3 -0
  37. checkpoint-2000/tokenizer.json +0 -0
  38. checkpoint-2000/tokenizer_config.json +17 -0
  39. checkpoint-2000/trainer_state.json +2034 -0
  40. checkpoint-2000/training_args.bin +3 -0
  41. checkpoint-2328/README.md +209 -0
  42. checkpoint-2328/adapter_config.json +43 -0
  43. checkpoint-2328/adapter_model.safetensors +3 -0
  44. checkpoint-2328/chat_template.jinja +6 -0
  45. checkpoint-2328/optimizer.pt +3 -0
  46. checkpoint-2328/rng_state.pth +3 -0
  47. checkpoint-2328/scaler.pt +3 -0
  48. checkpoint-2328/scheduler.pt +3 -0
  49. checkpoint-2328/tokenizer.json +0 -0
  50. checkpoint-2328/tokenizer_config.json +17 -0
README.md CHANGED
@@ -1,3 +1,62 @@
1
- ---
2
- license: cc-by-nc-4.0
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: HuggingFaceTB/SmolLM2-135M-Instruct
3
+ library_name: peft
4
+ model_name: smol-code-finetuned
5
+ tags:
6
+ - base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ licence: license
12
+ pipeline_tag: text-generation
13
+ ---
14
+
15
+ # Model Card for smol-code-finetuned
16
+
17
+ This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct).
18
+ It has been trained using [TRL](https://github.com/huggingface/trl).
19
+
20
+ ## Quick start
21
+
22
+ ```python
23
+ from transformers import pipeline
24
+
25
+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
26
+ generator = pipeline("text-generation", model="None", device="cuda")
27
+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
28
+ print(output["generated_text"])
29
+ ```
30
+
31
+ ## Training procedure
32
+
33
+
34
+
35
+
36
+
37
+ This model was trained with SFT.
38
+
39
+ ### Framework versions
40
+
41
+ - PEFT 0.18.1
42
+ - TRL: 0.29.0
43
+ - Transformers: 5.2.0
44
+ - Pytorch: 2.8.0+cu128
45
+ - Datasets: 4.6.0
46
+ - Tokenizers: 0.22.2
47
+
48
+ ## Citations
49
+
50
+
51
+
52
+ Cite TRL as:
53
+
54
+ ```bibtex
55
+ @software{vonwerra2020trl,
56
+ title = {{TRL: Transformers Reinforcement Learning}},
57
+ author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
58
+ license = {Apache-2.0},
59
+ url = {https://github.com/huggingface/trl},
60
+ year = {2020}
61
+ }
62
+ ```
adapter_config.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "HuggingFaceTB/SmolLM2-135M-Instruct",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.05,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "peft_version": "0.18.1",
27
+ "qalora_group_size": 16,
28
+ "r": 16,
29
+ "rank_pattern": {},
30
+ "revision": null,
31
+ "target_modules": [
32
+ "o_proj",
33
+ "k_proj",
34
+ "q_proj",
35
+ "v_proj"
36
+ ],
37
+ "target_parameters": null,
38
+ "task_type": "CAUSAL_LM",
39
+ "trainable_token_indices": null,
40
+ "use_dora": false,
41
+ "use_qalora": false,
42
+ "use_rslora": false
43
+ }
adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7fc9ea20edd42cffd8c5b32349900876962b89d0cf0b52aa2d24603f2324ff2a
3
+ size 7404368
chat_template.jinja ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
2
+ You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
3
+ ' }}{% endif %}{{'<|im_start|>' + message['role'] + '
4
+ ' + message['content'] + '<|im_end|>' + '
5
+ '}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
6
+ ' }}{% endif %}
checkpoint-1000/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: HuggingFaceTB/SmolLM2-135M-Instruct
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.18.1
checkpoint-1000/adapter_config.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "HuggingFaceTB/SmolLM2-135M-Instruct",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.05,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "peft_version": "0.18.1",
27
+ "qalora_group_size": 16,
28
+ "r": 16,
29
+ "rank_pattern": {},
30
+ "revision": null,
31
+ "target_modules": [
32
+ "o_proj",
33
+ "k_proj",
34
+ "q_proj",
35
+ "v_proj"
36
+ ],
37
+ "target_parameters": null,
38
+ "task_type": "CAUSAL_LM",
39
+ "trainable_token_indices": null,
40
+ "use_dora": false,
41
+ "use_qalora": false,
42
+ "use_rslora": false
43
+ }
checkpoint-1000/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1fb14f59fc57fecf96ea885954c9cb2822207733ba43670e1ff021fc31e68975
3
+ size 7404368
checkpoint-1000/chat_template.jinja ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
2
+ You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
3
+ ' }}{% endif %}{{'<|im_start|>' + message['role'] + '
4
+ ' + message['content'] + '<|im_end|>' + '
5
+ '}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
6
+ ' }}{% endif %}
checkpoint-1000/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a0e6afd6f6659f29d948d88153cd2bcc0efaea4885a62014c0d066cb8c366d52
3
+ size 14950667
checkpoint-1000/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8363b231e1dbed9a1bec5f309af311ec37800cc626a920ead274e4b95875d26d
3
+ size 14645
checkpoint-1000/scaler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:14ae2a2128444abab378aa06c09a61a84665f758fcc19fc46f5789b0bc1b5665
3
+ size 1383
checkpoint-1000/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:458c01d724b924da0f67feecdb71910c7d5255196b1858f519282fa895263558
3
+ size 1465
checkpoint-1000/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-1000/tokenizer_config.json ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": "<|im_start|>",
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|im_end|>",
7
+ "extra_special_tokens": [
8
+ "<|im_start|>",
9
+ "<|im_end|>"
10
+ ],
11
+ "is_local": false,
12
+ "model_max_length": 8192,
13
+ "pad_token": "<|im_end|>",
14
+ "tokenizer_class": "TokenizersBackend",
15
+ "unk_token": "<|endoftext|>",
16
+ "vocab_size": 49152
17
+ }
checkpoint-1000/trainer_state.json ADDED
@@ -0,0 +1,1034 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 0.8591065292096219,
6
+ "eval_steps": 500,
7
+ "global_step": 1000,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 1.1566081464290618,
14
+ "epoch": 0.00859106529209622,
15
+ "grad_norm": 0.15214066207408905,
16
+ "learning_rate": 0.00029884020618556697,
17
+ "loss": 1.226055908203125,
18
+ "mean_token_accuracy": 0.7206311166286469,
19
+ "num_tokens": 26541.0,
20
+ "step": 10
21
+ },
22
+ {
23
+ "entropy": 1.2002840995788575,
24
+ "epoch": 0.01718213058419244,
25
+ "grad_norm": 0.1833968460559845,
26
+ "learning_rate": 0.00029755154639175255,
27
+ "loss": 1.2007878303527832,
28
+ "mean_token_accuracy": 0.7304173231124877,
29
+ "num_tokens": 52280.0,
30
+ "step": 20
31
+ },
32
+ {
33
+ "entropy": 1.083837980031967,
34
+ "epoch": 0.02577319587628866,
35
+ "grad_norm": 0.15183115005493164,
36
+ "learning_rate": 0.0002962628865979381,
37
+ "loss": 1.1582960128784179,
38
+ "mean_token_accuracy": 0.7341346919536591,
39
+ "num_tokens": 79603.0,
40
+ "step": 30
41
+ },
42
+ {
43
+ "entropy": 0.9830838143825531,
44
+ "epoch": 0.03436426116838488,
45
+ "grad_norm": 0.1640024185180664,
46
+ "learning_rate": 0.00029497422680412364,
47
+ "loss": 0.989743423461914,
48
+ "mean_token_accuracy": 0.7652481079101563,
49
+ "num_tokens": 106301.0,
50
+ "step": 40
51
+ },
52
+ {
53
+ "entropy": 0.9237875938415527,
54
+ "epoch": 0.0429553264604811,
55
+ "grad_norm": 0.1496010422706604,
56
+ "learning_rate": 0.0002936855670103092,
57
+ "loss": 0.9489053726196289,
58
+ "mean_token_accuracy": 0.7682253539562225,
59
+ "num_tokens": 131791.0,
60
+ "step": 50
61
+ },
62
+ {
63
+ "entropy": 0.945674192905426,
64
+ "epoch": 0.05154639175257732,
65
+ "grad_norm": 0.16326577961444855,
66
+ "learning_rate": 0.0002923969072164948,
67
+ "loss": 0.9549709320068359,
68
+ "mean_token_accuracy": 0.7691607773303986,
69
+ "num_tokens": 156376.0,
70
+ "step": 60
71
+ },
72
+ {
73
+ "entropy": 0.9946551442146301,
74
+ "epoch": 0.06013745704467354,
75
+ "grad_norm": 0.14451684057712555,
76
+ "learning_rate": 0.00029110824742268037,
77
+ "loss": 0.9979846954345704,
78
+ "mean_token_accuracy": 0.7634965121746063,
79
+ "num_tokens": 183753.0,
80
+ "step": 70
81
+ },
82
+ {
83
+ "entropy": 0.9941512286663056,
84
+ "epoch": 0.06872852233676977,
85
+ "grad_norm": 0.15088772773742676,
86
+ "learning_rate": 0.00028981958762886595,
87
+ "loss": 0.9917948722839356,
88
+ "mean_token_accuracy": 0.7609466493129731,
89
+ "num_tokens": 209154.0,
90
+ "step": 80
91
+ },
92
+ {
93
+ "entropy": 0.899951022863388,
94
+ "epoch": 0.07731958762886598,
95
+ "grad_norm": 0.1635286509990692,
96
+ "learning_rate": 0.0002885309278350515,
97
+ "loss": 0.9084607124328613,
98
+ "mean_token_accuracy": 0.773688817024231,
99
+ "num_tokens": 232285.0,
100
+ "step": 90
101
+ },
102
+ {
103
+ "entropy": 0.9840596318244934,
104
+ "epoch": 0.0859106529209622,
105
+ "grad_norm": 0.1687513142824173,
106
+ "learning_rate": 0.0002872422680412371,
107
+ "loss": 1.005988883972168,
108
+ "mean_token_accuracy": 0.7565966248512268,
109
+ "num_tokens": 258165.0,
110
+ "step": 100
111
+ },
112
+ {
113
+ "entropy": 0.9256654202938079,
114
+ "epoch": 0.09450171821305842,
115
+ "grad_norm": 0.15977086126804352,
116
+ "learning_rate": 0.0002859536082474227,
117
+ "loss": 0.8968193054199218,
118
+ "mean_token_accuracy": 0.7728870630264282,
119
+ "num_tokens": 283613.0,
120
+ "step": 110
121
+ },
122
+ {
123
+ "entropy": 0.9430408120155335,
124
+ "epoch": 0.10309278350515463,
125
+ "grad_norm": 0.14840301871299744,
126
+ "learning_rate": 0.0002846649484536082,
127
+ "loss": 0.9234983444213867,
128
+ "mean_token_accuracy": 0.7652122378349304,
129
+ "num_tokens": 308257.0,
130
+ "step": 120
131
+ },
132
+ {
133
+ "entropy": 0.9537142395973206,
134
+ "epoch": 0.11168384879725086,
135
+ "grad_norm": 0.1754530817270279,
136
+ "learning_rate": 0.0002833762886597938,
137
+ "loss": 0.949033260345459,
138
+ "mean_token_accuracy": 0.7616437077522278,
139
+ "num_tokens": 333700.0,
140
+ "step": 130
141
+ },
142
+ {
143
+ "entropy": 0.9385378897190094,
144
+ "epoch": 0.12027491408934708,
145
+ "grad_norm": 0.16817958652973175,
146
+ "learning_rate": 0.00028208762886597935,
147
+ "loss": 0.9222366333007812,
148
+ "mean_token_accuracy": 0.7721138775348664,
149
+ "num_tokens": 359746.0,
150
+ "step": 140
151
+ },
152
+ {
153
+ "entropy": 0.8476657152175904,
154
+ "epoch": 0.12886597938144329,
155
+ "grad_norm": 0.15769453346729279,
156
+ "learning_rate": 0.0002807989690721649,
157
+ "loss": 0.8225071907043457,
158
+ "mean_token_accuracy": 0.7880932271480561,
159
+ "num_tokens": 383468.0,
160
+ "step": 150
161
+ },
162
+ {
163
+ "entropy": 0.8862757325172425,
164
+ "epoch": 0.13745704467353953,
165
+ "grad_norm": 0.17620904743671417,
166
+ "learning_rate": 0.0002795103092783505,
167
+ "loss": 0.8871653556823731,
168
+ "mean_token_accuracy": 0.7726393282413483,
169
+ "num_tokens": 409660.0,
170
+ "step": 160
171
+ },
172
+ {
173
+ "entropy": 0.9064472615718842,
174
+ "epoch": 0.14604810996563575,
175
+ "grad_norm": 0.15342549979686737,
176
+ "learning_rate": 0.0002782216494845361,
177
+ "loss": 0.9092922210693359,
178
+ "mean_token_accuracy": 0.7713103413581848,
179
+ "num_tokens": 437071.0,
180
+ "step": 170
181
+ },
182
+ {
183
+ "entropy": 0.9095455348491669,
184
+ "epoch": 0.15463917525773196,
185
+ "grad_norm": 0.17525391280651093,
186
+ "learning_rate": 0.00027693298969072165,
187
+ "loss": 0.8956180572509765,
188
+ "mean_token_accuracy": 0.7772489190101624,
189
+ "num_tokens": 460852.0,
190
+ "step": 180
191
+ },
192
+ {
193
+ "entropy": 0.8514094173908233,
194
+ "epoch": 0.16323024054982818,
195
+ "grad_norm": 0.15829823911190033,
196
+ "learning_rate": 0.0002756443298969072,
197
+ "loss": 0.8513413429260254,
198
+ "mean_token_accuracy": 0.779003256559372,
199
+ "num_tokens": 486114.0,
200
+ "step": 190
201
+ },
202
+ {
203
+ "entropy": 0.8995061337947845,
204
+ "epoch": 0.1718213058419244,
205
+ "grad_norm": 0.15622206032276154,
206
+ "learning_rate": 0.00027435567010309275,
207
+ "loss": 0.898340892791748,
208
+ "mean_token_accuracy": 0.7708433449268342,
209
+ "num_tokens": 512397.0,
210
+ "step": 200
211
+ },
212
+ {
213
+ "entropy": 0.8732269763946533,
214
+ "epoch": 0.18041237113402062,
215
+ "grad_norm": 0.17759552597999573,
216
+ "learning_rate": 0.0002730670103092783,
217
+ "loss": 0.8695596694946289,
218
+ "mean_token_accuracy": 0.7793173313140869,
219
+ "num_tokens": 536554.0,
220
+ "step": 210
221
+ },
222
+ {
223
+ "entropy": 0.9366106212139129,
224
+ "epoch": 0.18900343642611683,
225
+ "grad_norm": 0.1767597794532776,
226
+ "learning_rate": 0.0002717783505154639,
227
+ "loss": 0.9249121665954589,
228
+ "mean_token_accuracy": 0.7724991559982299,
229
+ "num_tokens": 561637.0,
230
+ "step": 220
231
+ },
232
+ {
233
+ "entropy": 0.8857321858406066,
234
+ "epoch": 0.19759450171821305,
235
+ "grad_norm": 0.20788735151290894,
236
+ "learning_rate": 0.0002704896907216495,
237
+ "loss": 0.8790401458740235,
238
+ "mean_token_accuracy": 0.7778670430183411,
239
+ "num_tokens": 586614.0,
240
+ "step": 230
241
+ },
242
+ {
243
+ "entropy": 0.9273143649101258,
244
+ "epoch": 0.20618556701030927,
245
+ "grad_norm": 0.16100187599658966,
246
+ "learning_rate": 0.00026920103092783505,
247
+ "loss": 0.9232232093811035,
248
+ "mean_token_accuracy": 0.7677364766597747,
249
+ "num_tokens": 612428.0,
250
+ "step": 240
251
+ },
252
+ {
253
+ "entropy": 0.8786617696285248,
254
+ "epoch": 0.21477663230240548,
255
+ "grad_norm": 0.1608886867761612,
256
+ "learning_rate": 0.00026791237113402063,
257
+ "loss": 0.8883329391479492,
258
+ "mean_token_accuracy": 0.7775621056556702,
259
+ "num_tokens": 638564.0,
260
+ "step": 250
261
+ },
262
+ {
263
+ "entropy": 0.9227964103221893,
264
+ "epoch": 0.22336769759450173,
265
+ "grad_norm": 0.1510680615901947,
266
+ "learning_rate": 0.00026662371134020615,
267
+ "loss": 0.9296038627624512,
268
+ "mean_token_accuracy": 0.7714014649391174,
269
+ "num_tokens": 665263.0,
270
+ "step": 260
271
+ },
272
+ {
273
+ "entropy": 0.9126189887523651,
274
+ "epoch": 0.23195876288659795,
275
+ "grad_norm": 0.16434459388256073,
276
+ "learning_rate": 0.00026533505154639173,
277
+ "loss": 0.9191689491271973,
278
+ "mean_token_accuracy": 0.7705935597419739,
279
+ "num_tokens": 691956.0,
280
+ "step": 270
281
+ },
282
+ {
283
+ "entropy": 0.8797955513000488,
284
+ "epoch": 0.24054982817869416,
285
+ "grad_norm": 0.1714707911014557,
286
+ "learning_rate": 0.0002640463917525773,
287
+ "loss": 0.8856119155883789,
288
+ "mean_token_accuracy": 0.7766761302947998,
289
+ "num_tokens": 716820.0,
290
+ "step": 280
291
+ },
292
+ {
293
+ "entropy": 0.877093505859375,
294
+ "epoch": 0.24914089347079038,
295
+ "grad_norm": 0.15326625108718872,
296
+ "learning_rate": 0.0002627577319587629,
297
+ "loss": 0.8566678047180176,
298
+ "mean_token_accuracy": 0.7812119901180268,
299
+ "num_tokens": 742221.0,
300
+ "step": 290
301
+ },
302
+ {
303
+ "entropy": 0.87042076587677,
304
+ "epoch": 0.25773195876288657,
305
+ "grad_norm": 0.1710847020149231,
306
+ "learning_rate": 0.00026146907216494846,
307
+ "loss": 0.8829298973083496,
308
+ "mean_token_accuracy": 0.7770332932472229,
309
+ "num_tokens": 765839.0,
310
+ "step": 300
311
+ },
312
+ {
313
+ "entropy": 0.8417458057403564,
314
+ "epoch": 0.2663230240549828,
315
+ "grad_norm": 0.1496821790933609,
316
+ "learning_rate": 0.00026018041237113403,
317
+ "loss": 0.8433545112609864,
318
+ "mean_token_accuracy": 0.7857938230037689,
319
+ "num_tokens": 791131.0,
320
+ "step": 310
321
+ },
322
+ {
323
+ "entropy": 0.9158999443054199,
324
+ "epoch": 0.27491408934707906,
325
+ "grad_norm": 0.17929035425186157,
326
+ "learning_rate": 0.00025889175257731955,
327
+ "loss": 0.9123490333557129,
328
+ "mean_token_accuracy": 0.775556480884552,
329
+ "num_tokens": 816984.0,
330
+ "step": 320
331
+ },
332
+ {
333
+ "entropy": 0.8314016401767731,
334
+ "epoch": 0.28350515463917525,
335
+ "grad_norm": 0.16051091253757477,
336
+ "learning_rate": 0.00025760309278350513,
337
+ "loss": 0.8279628753662109,
338
+ "mean_token_accuracy": 0.7862712442874908,
339
+ "num_tokens": 842220.0,
340
+ "step": 330
341
+ },
342
+ {
343
+ "entropy": 0.8677021145820618,
344
+ "epoch": 0.2920962199312715,
345
+ "grad_norm": 0.19157563149929047,
346
+ "learning_rate": 0.0002563144329896907,
347
+ "loss": 0.8552864074707032,
348
+ "mean_token_accuracy": 0.7835110187530517,
349
+ "num_tokens": 867868.0,
350
+ "step": 340
351
+ },
352
+ {
353
+ "entropy": 0.8919401407241822,
354
+ "epoch": 0.3006872852233677,
355
+ "grad_norm": 0.14258626103401184,
356
+ "learning_rate": 0.0002550257731958763,
357
+ "loss": 0.9019416809082031,
358
+ "mean_token_accuracy": 0.7760707855224609,
359
+ "num_tokens": 894676.0,
360
+ "step": 350
361
+ },
362
+ {
363
+ "entropy": 0.8415017545223236,
364
+ "epoch": 0.30927835051546393,
365
+ "grad_norm": 0.14830808341503143,
366
+ "learning_rate": 0.00025373711340206186,
367
+ "loss": 0.8072587013244629,
368
+ "mean_token_accuracy": 0.7912742376327515,
369
+ "num_tokens": 921632.0,
370
+ "step": 360
371
+ },
372
+ {
373
+ "entropy": 0.8371863842010498,
374
+ "epoch": 0.3178694158075601,
375
+ "grad_norm": 0.15538977086544037,
376
+ "learning_rate": 0.0002524484536082474,
377
+ "loss": 0.8308550834655761,
378
+ "mean_token_accuracy": 0.7849089920520782,
379
+ "num_tokens": 945955.0,
380
+ "step": 370
381
+ },
382
+ {
383
+ "entropy": 0.8338176369667053,
384
+ "epoch": 0.32646048109965636,
385
+ "grad_norm": 0.1711844801902771,
386
+ "learning_rate": 0.00025115979381443295,
387
+ "loss": 0.8479556083679199,
388
+ "mean_token_accuracy": 0.7850408136844635,
389
+ "num_tokens": 971508.0,
390
+ "step": 380
391
+ },
392
+ {
393
+ "entropy": 0.8876297354698182,
394
+ "epoch": 0.33505154639175255,
395
+ "grad_norm": 0.14902909100055695,
396
+ "learning_rate": 0.00024987113402061853,
397
+ "loss": 0.8821809768676758,
398
+ "mean_token_accuracy": 0.7764640510082245,
399
+ "num_tokens": 995124.0,
400
+ "step": 390
401
+ },
402
+ {
403
+ "entropy": 0.8212514638900756,
404
+ "epoch": 0.3436426116838488,
405
+ "grad_norm": 0.17950233817100525,
406
+ "learning_rate": 0.0002485824742268041,
407
+ "loss": 0.8255527496337891,
408
+ "mean_token_accuracy": 0.7870513379573822,
409
+ "num_tokens": 1019735.0,
410
+ "step": 400
411
+ },
412
+ {
413
+ "entropy": 0.9294387817382812,
414
+ "epoch": 0.35223367697594504,
415
+ "grad_norm": 0.15237703919410706,
416
+ "learning_rate": 0.0002472938144329897,
417
+ "loss": 0.9235431671142578,
418
+ "mean_token_accuracy": 0.7703655660152435,
419
+ "num_tokens": 1046295.0,
420
+ "step": 410
421
+ },
422
+ {
423
+ "entropy": 0.8429355442523956,
424
+ "epoch": 0.36082474226804123,
425
+ "grad_norm": 0.1589658558368683,
426
+ "learning_rate": 0.0002460051546391752,
427
+ "loss": 0.8329957008361817,
428
+ "mean_token_accuracy": 0.7897172749042511,
429
+ "num_tokens": 1072020.0,
430
+ "step": 420
431
+ },
432
+ {
433
+ "entropy": 0.8407810032367706,
434
+ "epoch": 0.3694158075601375,
435
+ "grad_norm": 0.17521116137504578,
436
+ "learning_rate": 0.0002447164948453608,
437
+ "loss": 0.8535200119018554,
438
+ "mean_token_accuracy": 0.7809976935386658,
439
+ "num_tokens": 1098420.0,
440
+ "step": 430
441
+ },
442
+ {
443
+ "entropy": 0.8515082120895385,
444
+ "epoch": 0.37800687285223367,
445
+ "grad_norm": 0.15218117833137512,
446
+ "learning_rate": 0.00024342783505154638,
447
+ "loss": 0.8459153175354004,
448
+ "mean_token_accuracy": 0.7833445191383361,
449
+ "num_tokens": 1124235.0,
450
+ "step": 440
451
+ },
452
+ {
453
+ "entropy": 0.8506015419960022,
454
+ "epoch": 0.3865979381443299,
455
+ "grad_norm": 0.1809423863887787,
456
+ "learning_rate": 0.00024213917525773193,
457
+ "loss": 0.8384982109069824,
458
+ "mean_token_accuracy": 0.7841372966766358,
459
+ "num_tokens": 1149636.0,
460
+ "step": 450
461
+ },
462
+ {
463
+ "entropy": 0.9110018074512481,
464
+ "epoch": 0.3951890034364261,
465
+ "grad_norm": 0.15085963904857635,
466
+ "learning_rate": 0.0002408505154639175,
467
+ "loss": 0.9191832542419434,
468
+ "mean_token_accuracy": 0.7757422685623169,
469
+ "num_tokens": 1173711.0,
470
+ "step": 460
471
+ },
472
+ {
473
+ "entropy": 0.9000201225280762,
474
+ "epoch": 0.40378006872852235,
475
+ "grad_norm": 0.16748838126659393,
476
+ "learning_rate": 0.00023956185567010308,
477
+ "loss": 0.9107230186462403,
478
+ "mean_token_accuracy": 0.7768173336982727,
479
+ "num_tokens": 1199340.0,
480
+ "step": 470
481
+ },
482
+ {
483
+ "entropy": 0.8751251935958863,
484
+ "epoch": 0.41237113402061853,
485
+ "grad_norm": 0.1593722254037857,
486
+ "learning_rate": 0.00023827319587628863,
487
+ "loss": 0.8804635047912598,
488
+ "mean_token_accuracy": 0.7814023733139038,
489
+ "num_tokens": 1226032.0,
490
+ "step": 480
491
+ },
492
+ {
493
+ "entropy": 0.8843371510505676,
494
+ "epoch": 0.4209621993127148,
495
+ "grad_norm": 0.12278851121664047,
496
+ "learning_rate": 0.0002369845360824742,
497
+ "loss": 0.8643080711364746,
498
+ "mean_token_accuracy": 0.7809630572795868,
499
+ "num_tokens": 1252769.0,
500
+ "step": 490
501
+ },
502
+ {
503
+ "entropy": 0.8834336459636688,
504
+ "epoch": 0.42955326460481097,
505
+ "grad_norm": 0.1367531716823578,
506
+ "learning_rate": 0.00023569587628865976,
507
+ "loss": 0.8869522094726563,
508
+ "mean_token_accuracy": 0.7775909185409546,
509
+ "num_tokens": 1278979.0,
510
+ "step": 500
511
+ },
512
+ {
513
+ "entropy": 0.9062523484230042,
514
+ "epoch": 0.4381443298969072,
515
+ "grad_norm": 0.14829133450984955,
516
+ "learning_rate": 0.00023440721649484533,
517
+ "loss": 0.9153310775756835,
518
+ "mean_token_accuracy": 0.7755910754203796,
519
+ "num_tokens": 1305141.0,
520
+ "step": 510
521
+ },
522
+ {
523
+ "entropy": 0.8907318472862243,
524
+ "epoch": 0.44673539518900346,
525
+ "grad_norm": 0.19640029966831207,
526
+ "learning_rate": 0.0002331185567010309,
527
+ "loss": 0.8736312866210938,
528
+ "mean_token_accuracy": 0.7823721885681152,
529
+ "num_tokens": 1333535.0,
530
+ "step": 520
531
+ },
532
+ {
533
+ "entropy": 0.9001505076885223,
534
+ "epoch": 0.45532646048109965,
535
+ "grad_norm": 0.1615416407585144,
536
+ "learning_rate": 0.00023182989690721648,
537
+ "loss": 0.9240070343017578,
538
+ "mean_token_accuracy": 0.7720246851444245,
539
+ "num_tokens": 1359100.0,
540
+ "step": 530
541
+ },
542
+ {
543
+ "entropy": 0.8964834988117218,
544
+ "epoch": 0.4639175257731959,
545
+ "grad_norm": 0.16148629784584045,
546
+ "learning_rate": 0.00023054123711340206,
547
+ "loss": 0.9040334701538086,
548
+ "mean_token_accuracy": 0.7697900414466858,
549
+ "num_tokens": 1386642.0,
550
+ "step": 540
551
+ },
552
+ {
553
+ "entropy": 0.9152944028377533,
554
+ "epoch": 0.4725085910652921,
555
+ "grad_norm": 0.16060467064380646,
556
+ "learning_rate": 0.00022925257731958758,
557
+ "loss": 0.9094497680664062,
558
+ "mean_token_accuracy": 0.771862530708313,
559
+ "num_tokens": 1412712.0,
560
+ "step": 550
561
+ },
562
+ {
563
+ "entropy": 0.8443439364433288,
564
+ "epoch": 0.48109965635738833,
565
+ "grad_norm": 0.16139253973960876,
566
+ "learning_rate": 0.00022796391752577316,
567
+ "loss": 0.8445466041564942,
568
+ "mean_token_accuracy": 0.7862408220767975,
569
+ "num_tokens": 1439208.0,
570
+ "step": 560
571
+ },
572
+ {
573
+ "entropy": 0.8669894218444825,
574
+ "epoch": 0.4896907216494845,
575
+ "grad_norm": 0.13149064779281616,
576
+ "learning_rate": 0.00022667525773195873,
577
+ "loss": 0.8791823387145996,
578
+ "mean_token_accuracy": 0.7771342694759369,
579
+ "num_tokens": 1466602.0,
580
+ "step": 570
581
+ },
582
+ {
583
+ "entropy": 0.8713687837123871,
584
+ "epoch": 0.49828178694158076,
585
+ "grad_norm": 0.15979787707328796,
586
+ "learning_rate": 0.0002253865979381443,
587
+ "loss": 0.8594341278076172,
588
+ "mean_token_accuracy": 0.7810667157173157,
589
+ "num_tokens": 1493048.0,
590
+ "step": 580
591
+ },
592
+ {
593
+ "entropy": 0.9155345737934113,
594
+ "epoch": 0.506872852233677,
595
+ "grad_norm": 0.18420852720737457,
596
+ "learning_rate": 0.00022409793814432989,
597
+ "loss": 0.915378475189209,
598
+ "mean_token_accuracy": 0.7724271833896637,
599
+ "num_tokens": 1517512.0,
600
+ "step": 590
601
+ },
602
+ {
603
+ "entropy": 0.891716331243515,
604
+ "epoch": 0.5154639175257731,
605
+ "grad_norm": 0.1665642410516739,
606
+ "learning_rate": 0.00022280927835051546,
607
+ "loss": 0.8950610160827637,
608
+ "mean_token_accuracy": 0.777475905418396,
609
+ "num_tokens": 1542765.0,
610
+ "step": 600
611
+ },
612
+ {
613
+ "entropy": 0.9547060489654541,
614
+ "epoch": 0.5240549828178694,
615
+ "grad_norm": 0.19496814906597137,
616
+ "learning_rate": 0.000221520618556701,
617
+ "loss": 0.9572202682495117,
618
+ "mean_token_accuracy": 0.7599396347999573,
619
+ "num_tokens": 1570704.0,
620
+ "step": 610
621
+ },
622
+ {
623
+ "entropy": 0.894726449251175,
624
+ "epoch": 0.5326460481099656,
625
+ "grad_norm": 0.162724569439888,
626
+ "learning_rate": 0.00022023195876288656,
627
+ "loss": 0.8876501083374023,
628
+ "mean_token_accuracy": 0.7815112411975861,
629
+ "num_tokens": 1594675.0,
630
+ "step": 620
631
+ },
632
+ {
633
+ "entropy": 0.8966928541660308,
634
+ "epoch": 0.5412371134020618,
635
+ "grad_norm": 0.1820981204509735,
636
+ "learning_rate": 0.00021894329896907214,
637
+ "loss": 0.8992101669311523,
638
+ "mean_token_accuracy": 0.7774552226066589,
639
+ "num_tokens": 1621381.0,
640
+ "step": 630
641
+ },
642
+ {
643
+ "entropy": 0.8320938229560852,
644
+ "epoch": 0.5498281786941581,
645
+ "grad_norm": 0.18641315400600433,
646
+ "learning_rate": 0.0002176546391752577,
647
+ "loss": 0.8333114624023438,
648
+ "mean_token_accuracy": 0.7836415946483613,
649
+ "num_tokens": 1646589.0,
650
+ "step": 640
651
+ },
652
+ {
653
+ "entropy": 0.9350853085517883,
654
+ "epoch": 0.5584192439862543,
655
+ "grad_norm": 0.15154911577701569,
656
+ "learning_rate": 0.0002163659793814433,
657
+ "loss": 0.9300890922546386,
658
+ "mean_token_accuracy": 0.7711542308330536,
659
+ "num_tokens": 1672281.0,
660
+ "step": 650
661
+ },
662
+ {
663
+ "entropy": 0.8372068881988526,
664
+ "epoch": 0.5670103092783505,
665
+ "grad_norm": 0.157988041639328,
666
+ "learning_rate": 0.00021507731958762884,
667
+ "loss": 0.8490092277526855,
668
+ "mean_token_accuracy": 0.7828391373157502,
669
+ "num_tokens": 1697203.0,
670
+ "step": 660
671
+ },
672
+ {
673
+ "entropy": 0.8175156831741333,
674
+ "epoch": 0.5756013745704467,
675
+ "grad_norm": 0.16615159809589386,
676
+ "learning_rate": 0.0002137886597938144,
677
+ "loss": 0.7962069988250733,
678
+ "mean_token_accuracy": 0.793628740310669,
679
+ "num_tokens": 1722977.0,
680
+ "step": 670
681
+ },
682
+ {
683
+ "entropy": 0.9293979525566101,
684
+ "epoch": 0.584192439862543,
685
+ "grad_norm": 0.13969369232654572,
686
+ "learning_rate": 0.0002125,
687
+ "loss": 0.9364766120910645,
688
+ "mean_token_accuracy": 0.7693193078041076,
689
+ "num_tokens": 1748869.0,
690
+ "step": 680
691
+ },
692
+ {
693
+ "entropy": 0.8958774268627167,
694
+ "epoch": 0.5927835051546392,
695
+ "grad_norm": 0.14425267279148102,
696
+ "learning_rate": 0.00021121134020618556,
697
+ "loss": 0.8842126846313476,
698
+ "mean_token_accuracy": 0.7759437382221221,
699
+ "num_tokens": 1774715.0,
700
+ "step": 690
701
+ },
702
+ {
703
+ "entropy": 0.929528433084488,
704
+ "epoch": 0.6013745704467354,
705
+ "grad_norm": 0.15875831246376038,
706
+ "learning_rate": 0.0002099226804123711,
707
+ "loss": 0.9353519439697265,
708
+ "mean_token_accuracy": 0.769885140657425,
709
+ "num_tokens": 1800430.0,
710
+ "step": 700
711
+ },
712
+ {
713
+ "entropy": 0.8651838064193725,
714
+ "epoch": 0.6099656357388317,
715
+ "grad_norm": 0.19464287161827087,
716
+ "learning_rate": 0.00020863402061855666,
717
+ "loss": 0.8678315162658692,
718
+ "mean_token_accuracy": 0.7783544957637787,
719
+ "num_tokens": 1824631.0,
720
+ "step": 710
721
+ },
722
+ {
723
+ "entropy": 0.874721884727478,
724
+ "epoch": 0.6185567010309279,
725
+ "grad_norm": 0.17015859484672546,
726
+ "learning_rate": 0.00020734536082474224,
727
+ "loss": 0.847961139678955,
728
+ "mean_token_accuracy": 0.7813271641731262,
729
+ "num_tokens": 1849593.0,
730
+ "step": 720
731
+ },
732
+ {
733
+ "entropy": 0.8706691145896912,
734
+ "epoch": 0.627147766323024,
735
+ "grad_norm": 0.15960460901260376,
736
+ "learning_rate": 0.00020605670103092781,
737
+ "loss": 0.8743976593017578,
738
+ "mean_token_accuracy": 0.7772314369678497,
739
+ "num_tokens": 1874593.0,
740
+ "step": 730
741
+ },
742
+ {
743
+ "entropy": 0.824019831418991,
744
+ "epoch": 0.6357388316151202,
745
+ "grad_norm": 0.16192808747291565,
746
+ "learning_rate": 0.0002047680412371134,
747
+ "loss": 0.8214377403259278,
748
+ "mean_token_accuracy": 0.7877136409282685,
749
+ "num_tokens": 1899592.0,
750
+ "step": 740
751
+ },
752
+ {
753
+ "entropy": 0.853943508863449,
754
+ "epoch": 0.6443298969072165,
755
+ "grad_norm": 0.14331476390361786,
756
+ "learning_rate": 0.00020347938144329897,
757
+ "loss": 0.8542372703552246,
758
+ "mean_token_accuracy": 0.7825242638587951,
759
+ "num_tokens": 1925105.0,
760
+ "step": 750
761
+ },
762
+ {
763
+ "entropy": 0.9206405401229858,
764
+ "epoch": 0.6529209621993127,
765
+ "grad_norm": 0.16269327700138092,
766
+ "learning_rate": 0.00020219072164948454,
767
+ "loss": 0.9131902694702149,
768
+ "mean_token_accuracy": 0.7722071945667267,
769
+ "num_tokens": 1952126.0,
770
+ "step": 760
771
+ },
772
+ {
773
+ "entropy": 0.8438917219638824,
774
+ "epoch": 0.6615120274914089,
775
+ "grad_norm": 0.18783624470233917,
776
+ "learning_rate": 0.00020090206185567006,
777
+ "loss": 0.8612903594970703,
778
+ "mean_token_accuracy": 0.7805359125137329,
779
+ "num_tokens": 1976727.0,
780
+ "step": 770
781
+ },
782
+ {
783
+ "entropy": 0.8782703518867493,
784
+ "epoch": 0.6701030927835051,
785
+ "grad_norm": 0.1749051958322525,
786
+ "learning_rate": 0.00019961340206185564,
787
+ "loss": 0.8877654075622559,
788
+ "mean_token_accuracy": 0.775201940536499,
789
+ "num_tokens": 2004204.0,
790
+ "step": 780
791
+ },
792
+ {
793
+ "entropy": 0.9036802053451538,
794
+ "epoch": 0.6786941580756014,
795
+ "grad_norm": 0.17728140950202942,
796
+ "learning_rate": 0.00019832474226804121,
797
+ "loss": 0.8946940422058105,
798
+ "mean_token_accuracy": 0.7781059086322785,
799
+ "num_tokens": 2031926.0,
800
+ "step": 790
801
+ },
802
+ {
803
+ "entropy": 0.9057375133037567,
804
+ "epoch": 0.6872852233676976,
805
+ "grad_norm": 0.1804327517747879,
806
+ "learning_rate": 0.0001970360824742268,
807
+ "loss": 0.9067662239074707,
808
+ "mean_token_accuracy": 0.7786577641963959,
809
+ "num_tokens": 2059628.0,
810
+ "step": 800
811
+ },
812
+ {
813
+ "entropy": 0.8421910405158997,
814
+ "epoch": 0.6958762886597938,
815
+ "grad_norm": 0.1570490598678589,
816
+ "learning_rate": 0.00019574742268041237,
817
+ "loss": 0.8346380233764649,
818
+ "mean_token_accuracy": 0.7839872419834137,
819
+ "num_tokens": 2083943.0,
820
+ "step": 810
821
+ },
822
+ {
823
+ "entropy": 0.8743102788925171,
824
+ "epoch": 0.7044673539518901,
825
+ "grad_norm": 0.1737687736749649,
826
+ "learning_rate": 0.00019445876288659794,
827
+ "loss": 0.8622690200805664,
828
+ "mean_token_accuracy": 0.7791435241699218,
829
+ "num_tokens": 2109444.0,
830
+ "step": 820
831
+ },
832
+ {
833
+ "entropy": 0.8926449298858643,
834
+ "epoch": 0.7130584192439863,
835
+ "grad_norm": 0.1972922682762146,
836
+ "learning_rate": 0.0001931701030927835,
837
+ "loss": 0.895075511932373,
838
+ "mean_token_accuracy": 0.7716107368469238,
839
+ "num_tokens": 2134209.0,
840
+ "step": 830
841
+ },
842
+ {
843
+ "entropy": 0.8712981760501861,
844
+ "epoch": 0.7216494845360825,
845
+ "grad_norm": 0.16580969095230103,
846
+ "learning_rate": 0.00019188144329896904,
847
+ "loss": 0.8615394592285156,
848
+ "mean_token_accuracy": 0.780440878868103,
849
+ "num_tokens": 2160322.0,
850
+ "step": 840
851
+ },
852
+ {
853
+ "entropy": 0.7872600853443146,
854
+ "epoch": 0.7302405498281787,
855
+ "grad_norm": 0.17089906334877014,
856
+ "learning_rate": 0.00019059278350515462,
857
+ "loss": 0.7738356590270996,
858
+ "mean_token_accuracy": 0.7984008610248565,
859
+ "num_tokens": 2186673.0,
860
+ "step": 850
861
+ },
862
+ {
863
+ "entropy": 0.8024394273757934,
864
+ "epoch": 0.738831615120275,
865
+ "grad_norm": 0.15015770494937897,
866
+ "learning_rate": 0.0001893041237113402,
867
+ "loss": 0.801088809967041,
868
+ "mean_token_accuracy": 0.7919513285160065,
869
+ "num_tokens": 2211288.0,
870
+ "step": 860
871
+ },
872
+ {
873
+ "entropy": 0.8289069414138794,
874
+ "epoch": 0.7474226804123711,
875
+ "grad_norm": 0.15804092586040497,
876
+ "learning_rate": 0.00018801546391752577,
877
+ "loss": 0.8228999137878418,
878
+ "mean_token_accuracy": 0.7900383174419403,
879
+ "num_tokens": 2235494.0,
880
+ "step": 870
881
+ },
882
+ {
883
+ "entropy": 0.8337613046169281,
884
+ "epoch": 0.7560137457044673,
885
+ "grad_norm": 0.16564202308654785,
886
+ "learning_rate": 0.00018672680412371132,
887
+ "loss": 0.8357515335083008,
888
+ "mean_token_accuracy": 0.7796721041202546,
889
+ "num_tokens": 2261853.0,
890
+ "step": 880
891
+ },
892
+ {
893
+ "entropy": 0.8514188408851624,
894
+ "epoch": 0.7646048109965635,
895
+ "grad_norm": 0.15763898193836212,
896
+ "learning_rate": 0.0001854381443298969,
897
+ "loss": 0.8596377372741699,
898
+ "mean_token_accuracy": 0.781908392906189,
899
+ "num_tokens": 2288235.0,
900
+ "step": 890
901
+ },
902
+ {
903
+ "entropy": 0.8182059168815613,
904
+ "epoch": 0.7731958762886598,
905
+ "grad_norm": 0.14843082427978516,
906
+ "learning_rate": 0.00018414948453608247,
907
+ "loss": 0.8056727409362793,
908
+ "mean_token_accuracy": 0.7887085616588593,
909
+ "num_tokens": 2312636.0,
910
+ "step": 900
911
+ },
912
+ {
913
+ "entropy": 0.8019815027713776,
914
+ "epoch": 0.781786941580756,
915
+ "grad_norm": 0.1444498896598816,
916
+ "learning_rate": 0.00018286082474226804,
917
+ "loss": 0.7791801452636719,
918
+ "mean_token_accuracy": 0.7921311616897583,
919
+ "num_tokens": 2337590.0,
920
+ "step": 910
921
+ },
922
+ {
923
+ "entropy": 0.8811851978302002,
924
+ "epoch": 0.7903780068728522,
925
+ "grad_norm": 0.20295798778533936,
926
+ "learning_rate": 0.0001815721649484536,
927
+ "loss": 0.9093887329101562,
928
+ "mean_token_accuracy": 0.7765615165233613,
929
+ "num_tokens": 2364733.0,
930
+ "step": 920
931
+ },
932
+ {
933
+ "entropy": 0.8845545649528503,
934
+ "epoch": 0.7989690721649485,
935
+ "grad_norm": 0.16211840510368347,
936
+ "learning_rate": 0.00018028350515463914,
937
+ "loss": 0.87130765914917,
938
+ "mean_token_accuracy": 0.773591297864914,
939
+ "num_tokens": 2389138.0,
940
+ "step": 930
941
+ },
942
+ {
943
+ "entropy": 0.8366316437721253,
944
+ "epoch": 0.8075601374570447,
945
+ "grad_norm": 0.1796175092458725,
946
+ "learning_rate": 0.00017899484536082472,
947
+ "loss": 0.8393503189086914,
948
+ "mean_token_accuracy": 0.7834821224212647,
949
+ "num_tokens": 2416288.0,
950
+ "step": 940
951
+ },
952
+ {
953
+ "entropy": 0.8687749207019806,
954
+ "epoch": 0.8161512027491409,
955
+ "grad_norm": 0.17602433264255524,
956
+ "learning_rate": 0.0001777061855670103,
957
+ "loss": 0.8831927299499511,
958
+ "mean_token_accuracy": 0.7806714773178101,
959
+ "num_tokens": 2442282.0,
960
+ "step": 950
961
+ },
962
+ {
963
+ "entropy": 0.8602447748184204,
964
+ "epoch": 0.8247422680412371,
965
+ "grad_norm": 0.14779216051101685,
966
+ "learning_rate": 0.00017641752577319587,
967
+ "loss": 0.8557798385620117,
968
+ "mean_token_accuracy": 0.7800233542919159,
969
+ "num_tokens": 2469865.0,
970
+ "step": 960
971
+ },
972
+ {
973
+ "entropy": 0.8987692832946778,
974
+ "epoch": 0.8333333333333334,
975
+ "grad_norm": 0.16065306961536407,
976
+ "learning_rate": 0.00017512886597938145,
977
+ "loss": 0.8930519104003907,
978
+ "mean_token_accuracy": 0.7771966874599456,
979
+ "num_tokens": 2497716.0,
980
+ "step": 970
981
+ },
982
+ {
983
+ "entropy": 0.847240948677063,
984
+ "epoch": 0.8419243986254296,
985
+ "grad_norm": 0.16063280403614044,
986
+ "learning_rate": 0.00017384020618556702,
987
+ "loss": 0.8212727546691895,
988
+ "mean_token_accuracy": 0.7900629937648773,
989
+ "num_tokens": 2523292.0,
990
+ "step": 980
991
+ },
992
+ {
993
+ "entropy": 0.8685175359249115,
994
+ "epoch": 0.8505154639175257,
995
+ "grad_norm": 0.19293011724948883,
996
+ "learning_rate": 0.00017255154639175254,
997
+ "loss": 0.8839738845825196,
998
+ "mean_token_accuracy": 0.7780547857284545,
999
+ "num_tokens": 2547529.0,
1000
+ "step": 990
1001
+ },
1002
+ {
1003
+ "entropy": 0.8589172720909118,
1004
+ "epoch": 0.8591065292096219,
1005
+ "grad_norm": 0.13081640005111694,
1006
+ "learning_rate": 0.00017126288659793812,
1007
+ "loss": 0.8760468482971191,
1008
+ "mean_token_accuracy": 0.7810062050819397,
1009
+ "num_tokens": 2574943.0,
1010
+ "step": 1000
1011
+ }
1012
+ ],
1013
+ "logging_steps": 10,
1014
+ "max_steps": 2328,
1015
+ "num_input_tokens_seen": 0,
1016
+ "num_train_epochs": 2,
1017
+ "save_steps": 500,
1018
+ "stateful_callbacks": {
1019
+ "TrainerControl": {
1020
+ "args": {
1021
+ "should_epoch_stop": false,
1022
+ "should_evaluate": false,
1023
+ "should_log": false,
1024
+ "should_save": true,
1025
+ "should_training_stop": false
1026
+ },
1027
+ "attributes": {}
1028
+ }
1029
+ },
1030
+ "total_flos": 4219358487773184.0,
1031
+ "train_batch_size": 16,
1032
+ "trial_name": null,
1033
+ "trial_params": null
1034
+ }
checkpoint-1000/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f2af45870b4c4d611381846df6bdd4a32cb292a7b5897b79b7e5f196be57c596
3
+ size 5585
checkpoint-1500/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: HuggingFaceTB/SmolLM2-135M-Instruct
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.18.1
checkpoint-1500/adapter_config.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "HuggingFaceTB/SmolLM2-135M-Instruct",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.05,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "peft_version": "0.18.1",
27
+ "qalora_group_size": 16,
28
+ "r": 16,
29
+ "rank_pattern": {},
30
+ "revision": null,
31
+ "target_modules": [
32
+ "o_proj",
33
+ "k_proj",
34
+ "q_proj",
35
+ "v_proj"
36
+ ],
37
+ "target_parameters": null,
38
+ "task_type": "CAUSAL_LM",
39
+ "trainable_token_indices": null,
40
+ "use_dora": false,
41
+ "use_qalora": false,
42
+ "use_rslora": false
43
+ }
checkpoint-1500/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4a8a8ac95f14935d4b0f3b9b714a958644a969c8852eec5d6a0cb9a0d964922b
3
+ size 7404368
checkpoint-1500/chat_template.jinja ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
2
+ You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
3
+ ' }}{% endif %}{{'<|im_start|>' + message['role'] + '
4
+ ' + message['content'] + '<|im_end|>' + '
5
+ '}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
6
+ ' }}{% endif %}
checkpoint-1500/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e6a377fa5af5cf2d7038f86402b71ce7234829f74d7bbb08c33f589517454578
3
+ size 14950667
checkpoint-1500/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d8ea63bc095d2dce18c525cee8c55d93344b51e8fec1a86f8f2f1df60fbe2b74
3
+ size 14645
checkpoint-1500/scaler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ca372268f4fa9335030c0cb7aedb6cdba75f457da50e7a4034abb1a2d0843689
3
+ size 1383
checkpoint-1500/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cdaed8eaa91e201684212ae8f5031fb818b28a8dd09b8187a56ebc01552b1a6d
3
+ size 1465
checkpoint-1500/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-1500/tokenizer_config.json ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": "<|im_start|>",
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|im_end|>",
7
+ "extra_special_tokens": [
8
+ "<|im_start|>",
9
+ "<|im_end|>"
10
+ ],
11
+ "is_local": false,
12
+ "model_max_length": 8192,
13
+ "pad_token": "<|im_end|>",
14
+ "tokenizer_class": "TokenizersBackend",
15
+ "unk_token": "<|endoftext|>",
16
+ "vocab_size": 49152
17
+ }
checkpoint-1500/trainer_state.json ADDED
@@ -0,0 +1,1534 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 1.2886597938144329,
6
+ "eval_steps": 500,
7
+ "global_step": 1500,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 1.1566081464290618,
14
+ "epoch": 0.00859106529209622,
15
+ "grad_norm": 0.15214066207408905,
16
+ "learning_rate": 0.00029884020618556697,
17
+ "loss": 1.226055908203125,
18
+ "mean_token_accuracy": 0.7206311166286469,
19
+ "num_tokens": 26541.0,
20
+ "step": 10
21
+ },
22
+ {
23
+ "entropy": 1.2002840995788575,
24
+ "epoch": 0.01718213058419244,
25
+ "grad_norm": 0.1833968460559845,
26
+ "learning_rate": 0.00029755154639175255,
27
+ "loss": 1.2007878303527832,
28
+ "mean_token_accuracy": 0.7304173231124877,
29
+ "num_tokens": 52280.0,
30
+ "step": 20
31
+ },
32
+ {
33
+ "entropy": 1.083837980031967,
34
+ "epoch": 0.02577319587628866,
35
+ "grad_norm": 0.15183115005493164,
36
+ "learning_rate": 0.0002962628865979381,
37
+ "loss": 1.1582960128784179,
38
+ "mean_token_accuracy": 0.7341346919536591,
39
+ "num_tokens": 79603.0,
40
+ "step": 30
41
+ },
42
+ {
43
+ "entropy": 0.9830838143825531,
44
+ "epoch": 0.03436426116838488,
45
+ "grad_norm": 0.1640024185180664,
46
+ "learning_rate": 0.00029497422680412364,
47
+ "loss": 0.989743423461914,
48
+ "mean_token_accuracy": 0.7652481079101563,
49
+ "num_tokens": 106301.0,
50
+ "step": 40
51
+ },
52
+ {
53
+ "entropy": 0.9237875938415527,
54
+ "epoch": 0.0429553264604811,
55
+ "grad_norm": 0.1496010422706604,
56
+ "learning_rate": 0.0002936855670103092,
57
+ "loss": 0.9489053726196289,
58
+ "mean_token_accuracy": 0.7682253539562225,
59
+ "num_tokens": 131791.0,
60
+ "step": 50
61
+ },
62
+ {
63
+ "entropy": 0.945674192905426,
64
+ "epoch": 0.05154639175257732,
65
+ "grad_norm": 0.16326577961444855,
66
+ "learning_rate": 0.0002923969072164948,
67
+ "loss": 0.9549709320068359,
68
+ "mean_token_accuracy": 0.7691607773303986,
69
+ "num_tokens": 156376.0,
70
+ "step": 60
71
+ },
72
+ {
73
+ "entropy": 0.9946551442146301,
74
+ "epoch": 0.06013745704467354,
75
+ "grad_norm": 0.14451684057712555,
76
+ "learning_rate": 0.00029110824742268037,
77
+ "loss": 0.9979846954345704,
78
+ "mean_token_accuracy": 0.7634965121746063,
79
+ "num_tokens": 183753.0,
80
+ "step": 70
81
+ },
82
+ {
83
+ "entropy": 0.9941512286663056,
84
+ "epoch": 0.06872852233676977,
85
+ "grad_norm": 0.15088772773742676,
86
+ "learning_rate": 0.00028981958762886595,
87
+ "loss": 0.9917948722839356,
88
+ "mean_token_accuracy": 0.7609466493129731,
89
+ "num_tokens": 209154.0,
90
+ "step": 80
91
+ },
92
+ {
93
+ "entropy": 0.899951022863388,
94
+ "epoch": 0.07731958762886598,
95
+ "grad_norm": 0.1635286509990692,
96
+ "learning_rate": 0.0002885309278350515,
97
+ "loss": 0.9084607124328613,
98
+ "mean_token_accuracy": 0.773688817024231,
99
+ "num_tokens": 232285.0,
100
+ "step": 90
101
+ },
102
+ {
103
+ "entropy": 0.9840596318244934,
104
+ "epoch": 0.0859106529209622,
105
+ "grad_norm": 0.1687513142824173,
106
+ "learning_rate": 0.0002872422680412371,
107
+ "loss": 1.005988883972168,
108
+ "mean_token_accuracy": 0.7565966248512268,
109
+ "num_tokens": 258165.0,
110
+ "step": 100
111
+ },
112
+ {
113
+ "entropy": 0.9256654202938079,
114
+ "epoch": 0.09450171821305842,
115
+ "grad_norm": 0.15977086126804352,
116
+ "learning_rate": 0.0002859536082474227,
117
+ "loss": 0.8968193054199218,
118
+ "mean_token_accuracy": 0.7728870630264282,
119
+ "num_tokens": 283613.0,
120
+ "step": 110
121
+ },
122
+ {
123
+ "entropy": 0.9430408120155335,
124
+ "epoch": 0.10309278350515463,
125
+ "grad_norm": 0.14840301871299744,
126
+ "learning_rate": 0.0002846649484536082,
127
+ "loss": 0.9234983444213867,
128
+ "mean_token_accuracy": 0.7652122378349304,
129
+ "num_tokens": 308257.0,
130
+ "step": 120
131
+ },
132
+ {
133
+ "entropy": 0.9537142395973206,
134
+ "epoch": 0.11168384879725086,
135
+ "grad_norm": 0.1754530817270279,
136
+ "learning_rate": 0.0002833762886597938,
137
+ "loss": 0.949033260345459,
138
+ "mean_token_accuracy": 0.7616437077522278,
139
+ "num_tokens": 333700.0,
140
+ "step": 130
141
+ },
142
+ {
143
+ "entropy": 0.9385378897190094,
144
+ "epoch": 0.12027491408934708,
145
+ "grad_norm": 0.16817958652973175,
146
+ "learning_rate": 0.00028208762886597935,
147
+ "loss": 0.9222366333007812,
148
+ "mean_token_accuracy": 0.7721138775348664,
149
+ "num_tokens": 359746.0,
150
+ "step": 140
151
+ },
152
+ {
153
+ "entropy": 0.8476657152175904,
154
+ "epoch": 0.12886597938144329,
155
+ "grad_norm": 0.15769453346729279,
156
+ "learning_rate": 0.0002807989690721649,
157
+ "loss": 0.8225071907043457,
158
+ "mean_token_accuracy": 0.7880932271480561,
159
+ "num_tokens": 383468.0,
160
+ "step": 150
161
+ },
162
+ {
163
+ "entropy": 0.8862757325172425,
164
+ "epoch": 0.13745704467353953,
165
+ "grad_norm": 0.17620904743671417,
166
+ "learning_rate": 0.0002795103092783505,
167
+ "loss": 0.8871653556823731,
168
+ "mean_token_accuracy": 0.7726393282413483,
169
+ "num_tokens": 409660.0,
170
+ "step": 160
171
+ },
172
+ {
173
+ "entropy": 0.9064472615718842,
174
+ "epoch": 0.14604810996563575,
175
+ "grad_norm": 0.15342549979686737,
176
+ "learning_rate": 0.0002782216494845361,
177
+ "loss": 0.9092922210693359,
178
+ "mean_token_accuracy": 0.7713103413581848,
179
+ "num_tokens": 437071.0,
180
+ "step": 170
181
+ },
182
+ {
183
+ "entropy": 0.9095455348491669,
184
+ "epoch": 0.15463917525773196,
185
+ "grad_norm": 0.17525391280651093,
186
+ "learning_rate": 0.00027693298969072165,
187
+ "loss": 0.8956180572509765,
188
+ "mean_token_accuracy": 0.7772489190101624,
189
+ "num_tokens": 460852.0,
190
+ "step": 180
191
+ },
192
+ {
193
+ "entropy": 0.8514094173908233,
194
+ "epoch": 0.16323024054982818,
195
+ "grad_norm": 0.15829823911190033,
196
+ "learning_rate": 0.0002756443298969072,
197
+ "loss": 0.8513413429260254,
198
+ "mean_token_accuracy": 0.779003256559372,
199
+ "num_tokens": 486114.0,
200
+ "step": 190
201
+ },
202
+ {
203
+ "entropy": 0.8995061337947845,
204
+ "epoch": 0.1718213058419244,
205
+ "grad_norm": 0.15622206032276154,
206
+ "learning_rate": 0.00027435567010309275,
207
+ "loss": 0.898340892791748,
208
+ "mean_token_accuracy": 0.7708433449268342,
209
+ "num_tokens": 512397.0,
210
+ "step": 200
211
+ },
212
+ {
213
+ "entropy": 0.8732269763946533,
214
+ "epoch": 0.18041237113402062,
215
+ "grad_norm": 0.17759552597999573,
216
+ "learning_rate": 0.0002730670103092783,
217
+ "loss": 0.8695596694946289,
218
+ "mean_token_accuracy": 0.7793173313140869,
219
+ "num_tokens": 536554.0,
220
+ "step": 210
221
+ },
222
+ {
223
+ "entropy": 0.9366106212139129,
224
+ "epoch": 0.18900343642611683,
225
+ "grad_norm": 0.1767597794532776,
226
+ "learning_rate": 0.0002717783505154639,
227
+ "loss": 0.9249121665954589,
228
+ "mean_token_accuracy": 0.7724991559982299,
229
+ "num_tokens": 561637.0,
230
+ "step": 220
231
+ },
232
+ {
233
+ "entropy": 0.8857321858406066,
234
+ "epoch": 0.19759450171821305,
235
+ "grad_norm": 0.20788735151290894,
236
+ "learning_rate": 0.0002704896907216495,
237
+ "loss": 0.8790401458740235,
238
+ "mean_token_accuracy": 0.7778670430183411,
239
+ "num_tokens": 586614.0,
240
+ "step": 230
241
+ },
242
+ {
243
+ "entropy": 0.9273143649101258,
244
+ "epoch": 0.20618556701030927,
245
+ "grad_norm": 0.16100187599658966,
246
+ "learning_rate": 0.00026920103092783505,
247
+ "loss": 0.9232232093811035,
248
+ "mean_token_accuracy": 0.7677364766597747,
249
+ "num_tokens": 612428.0,
250
+ "step": 240
251
+ },
252
+ {
253
+ "entropy": 0.8786617696285248,
254
+ "epoch": 0.21477663230240548,
255
+ "grad_norm": 0.1608886867761612,
256
+ "learning_rate": 0.00026791237113402063,
257
+ "loss": 0.8883329391479492,
258
+ "mean_token_accuracy": 0.7775621056556702,
259
+ "num_tokens": 638564.0,
260
+ "step": 250
261
+ },
262
+ {
263
+ "entropy": 0.9227964103221893,
264
+ "epoch": 0.22336769759450173,
265
+ "grad_norm": 0.1510680615901947,
266
+ "learning_rate": 0.00026662371134020615,
267
+ "loss": 0.9296038627624512,
268
+ "mean_token_accuracy": 0.7714014649391174,
269
+ "num_tokens": 665263.0,
270
+ "step": 260
271
+ },
272
+ {
273
+ "entropy": 0.9126189887523651,
274
+ "epoch": 0.23195876288659795,
275
+ "grad_norm": 0.16434459388256073,
276
+ "learning_rate": 0.00026533505154639173,
277
+ "loss": 0.9191689491271973,
278
+ "mean_token_accuracy": 0.7705935597419739,
279
+ "num_tokens": 691956.0,
280
+ "step": 270
281
+ },
282
+ {
283
+ "entropy": 0.8797955513000488,
284
+ "epoch": 0.24054982817869416,
285
+ "grad_norm": 0.1714707911014557,
286
+ "learning_rate": 0.0002640463917525773,
287
+ "loss": 0.8856119155883789,
288
+ "mean_token_accuracy": 0.7766761302947998,
289
+ "num_tokens": 716820.0,
290
+ "step": 280
291
+ },
292
+ {
293
+ "entropy": 0.877093505859375,
294
+ "epoch": 0.24914089347079038,
295
+ "grad_norm": 0.15326625108718872,
296
+ "learning_rate": 0.0002627577319587629,
297
+ "loss": 0.8566678047180176,
298
+ "mean_token_accuracy": 0.7812119901180268,
299
+ "num_tokens": 742221.0,
300
+ "step": 290
301
+ },
302
+ {
303
+ "entropy": 0.87042076587677,
304
+ "epoch": 0.25773195876288657,
305
+ "grad_norm": 0.1710847020149231,
306
+ "learning_rate": 0.00026146907216494846,
307
+ "loss": 0.8829298973083496,
308
+ "mean_token_accuracy": 0.7770332932472229,
309
+ "num_tokens": 765839.0,
310
+ "step": 300
311
+ },
312
+ {
313
+ "entropy": 0.8417458057403564,
314
+ "epoch": 0.2663230240549828,
315
+ "grad_norm": 0.1496821790933609,
316
+ "learning_rate": 0.00026018041237113403,
317
+ "loss": 0.8433545112609864,
318
+ "mean_token_accuracy": 0.7857938230037689,
319
+ "num_tokens": 791131.0,
320
+ "step": 310
321
+ },
322
+ {
323
+ "entropy": 0.9158999443054199,
324
+ "epoch": 0.27491408934707906,
325
+ "grad_norm": 0.17929035425186157,
326
+ "learning_rate": 0.00025889175257731955,
327
+ "loss": 0.9123490333557129,
328
+ "mean_token_accuracy": 0.775556480884552,
329
+ "num_tokens": 816984.0,
330
+ "step": 320
331
+ },
332
+ {
333
+ "entropy": 0.8314016401767731,
334
+ "epoch": 0.28350515463917525,
335
+ "grad_norm": 0.16051091253757477,
336
+ "learning_rate": 0.00025760309278350513,
337
+ "loss": 0.8279628753662109,
338
+ "mean_token_accuracy": 0.7862712442874908,
339
+ "num_tokens": 842220.0,
340
+ "step": 330
341
+ },
342
+ {
343
+ "entropy": 0.8677021145820618,
344
+ "epoch": 0.2920962199312715,
345
+ "grad_norm": 0.19157563149929047,
346
+ "learning_rate": 0.0002563144329896907,
347
+ "loss": 0.8552864074707032,
348
+ "mean_token_accuracy": 0.7835110187530517,
349
+ "num_tokens": 867868.0,
350
+ "step": 340
351
+ },
352
+ {
353
+ "entropy": 0.8919401407241822,
354
+ "epoch": 0.3006872852233677,
355
+ "grad_norm": 0.14258626103401184,
356
+ "learning_rate": 0.0002550257731958763,
357
+ "loss": 0.9019416809082031,
358
+ "mean_token_accuracy": 0.7760707855224609,
359
+ "num_tokens": 894676.0,
360
+ "step": 350
361
+ },
362
+ {
363
+ "entropy": 0.8415017545223236,
364
+ "epoch": 0.30927835051546393,
365
+ "grad_norm": 0.14830808341503143,
366
+ "learning_rate": 0.00025373711340206186,
367
+ "loss": 0.8072587013244629,
368
+ "mean_token_accuracy": 0.7912742376327515,
369
+ "num_tokens": 921632.0,
370
+ "step": 360
371
+ },
372
+ {
373
+ "entropy": 0.8371863842010498,
374
+ "epoch": 0.3178694158075601,
375
+ "grad_norm": 0.15538977086544037,
376
+ "learning_rate": 0.0002524484536082474,
377
+ "loss": 0.8308550834655761,
378
+ "mean_token_accuracy": 0.7849089920520782,
379
+ "num_tokens": 945955.0,
380
+ "step": 370
381
+ },
382
+ {
383
+ "entropy": 0.8338176369667053,
384
+ "epoch": 0.32646048109965636,
385
+ "grad_norm": 0.1711844801902771,
386
+ "learning_rate": 0.00025115979381443295,
387
+ "loss": 0.8479556083679199,
388
+ "mean_token_accuracy": 0.7850408136844635,
389
+ "num_tokens": 971508.0,
390
+ "step": 380
391
+ },
392
+ {
393
+ "entropy": 0.8876297354698182,
394
+ "epoch": 0.33505154639175255,
395
+ "grad_norm": 0.14902909100055695,
396
+ "learning_rate": 0.00024987113402061853,
397
+ "loss": 0.8821809768676758,
398
+ "mean_token_accuracy": 0.7764640510082245,
399
+ "num_tokens": 995124.0,
400
+ "step": 390
401
+ },
402
+ {
403
+ "entropy": 0.8212514638900756,
404
+ "epoch": 0.3436426116838488,
405
+ "grad_norm": 0.17950233817100525,
406
+ "learning_rate": 0.0002485824742268041,
407
+ "loss": 0.8255527496337891,
408
+ "mean_token_accuracy": 0.7870513379573822,
409
+ "num_tokens": 1019735.0,
410
+ "step": 400
411
+ },
412
+ {
413
+ "entropy": 0.9294387817382812,
414
+ "epoch": 0.35223367697594504,
415
+ "grad_norm": 0.15237703919410706,
416
+ "learning_rate": 0.0002472938144329897,
417
+ "loss": 0.9235431671142578,
418
+ "mean_token_accuracy": 0.7703655660152435,
419
+ "num_tokens": 1046295.0,
420
+ "step": 410
421
+ },
422
+ {
423
+ "entropy": 0.8429355442523956,
424
+ "epoch": 0.36082474226804123,
425
+ "grad_norm": 0.1589658558368683,
426
+ "learning_rate": 0.0002460051546391752,
427
+ "loss": 0.8329957008361817,
428
+ "mean_token_accuracy": 0.7897172749042511,
429
+ "num_tokens": 1072020.0,
430
+ "step": 420
431
+ },
432
+ {
433
+ "entropy": 0.8407810032367706,
434
+ "epoch": 0.3694158075601375,
435
+ "grad_norm": 0.17521116137504578,
436
+ "learning_rate": 0.0002447164948453608,
437
+ "loss": 0.8535200119018554,
438
+ "mean_token_accuracy": 0.7809976935386658,
439
+ "num_tokens": 1098420.0,
440
+ "step": 430
441
+ },
442
+ {
443
+ "entropy": 0.8515082120895385,
444
+ "epoch": 0.37800687285223367,
445
+ "grad_norm": 0.15218117833137512,
446
+ "learning_rate": 0.00024342783505154638,
447
+ "loss": 0.8459153175354004,
448
+ "mean_token_accuracy": 0.7833445191383361,
449
+ "num_tokens": 1124235.0,
450
+ "step": 440
451
+ },
452
+ {
453
+ "entropy": 0.8506015419960022,
454
+ "epoch": 0.3865979381443299,
455
+ "grad_norm": 0.1809423863887787,
456
+ "learning_rate": 0.00024213917525773193,
457
+ "loss": 0.8384982109069824,
458
+ "mean_token_accuracy": 0.7841372966766358,
459
+ "num_tokens": 1149636.0,
460
+ "step": 450
461
+ },
462
+ {
463
+ "entropy": 0.9110018074512481,
464
+ "epoch": 0.3951890034364261,
465
+ "grad_norm": 0.15085963904857635,
466
+ "learning_rate": 0.0002408505154639175,
467
+ "loss": 0.9191832542419434,
468
+ "mean_token_accuracy": 0.7757422685623169,
469
+ "num_tokens": 1173711.0,
470
+ "step": 460
471
+ },
472
+ {
473
+ "entropy": 0.9000201225280762,
474
+ "epoch": 0.40378006872852235,
475
+ "grad_norm": 0.16748838126659393,
476
+ "learning_rate": 0.00023956185567010308,
477
+ "loss": 0.9107230186462403,
478
+ "mean_token_accuracy": 0.7768173336982727,
479
+ "num_tokens": 1199340.0,
480
+ "step": 470
481
+ },
482
+ {
483
+ "entropy": 0.8751251935958863,
484
+ "epoch": 0.41237113402061853,
485
+ "grad_norm": 0.1593722254037857,
486
+ "learning_rate": 0.00023827319587628863,
487
+ "loss": 0.8804635047912598,
488
+ "mean_token_accuracy": 0.7814023733139038,
489
+ "num_tokens": 1226032.0,
490
+ "step": 480
491
+ },
492
+ {
493
+ "entropy": 0.8843371510505676,
494
+ "epoch": 0.4209621993127148,
495
+ "grad_norm": 0.12278851121664047,
496
+ "learning_rate": 0.0002369845360824742,
497
+ "loss": 0.8643080711364746,
498
+ "mean_token_accuracy": 0.7809630572795868,
499
+ "num_tokens": 1252769.0,
500
+ "step": 490
501
+ },
502
+ {
503
+ "entropy": 0.8834336459636688,
504
+ "epoch": 0.42955326460481097,
505
+ "grad_norm": 0.1367531716823578,
506
+ "learning_rate": 0.00023569587628865976,
507
+ "loss": 0.8869522094726563,
508
+ "mean_token_accuracy": 0.7775909185409546,
509
+ "num_tokens": 1278979.0,
510
+ "step": 500
511
+ },
512
+ {
513
+ "entropy": 0.9062523484230042,
514
+ "epoch": 0.4381443298969072,
515
+ "grad_norm": 0.14829133450984955,
516
+ "learning_rate": 0.00023440721649484533,
517
+ "loss": 0.9153310775756835,
518
+ "mean_token_accuracy": 0.7755910754203796,
519
+ "num_tokens": 1305141.0,
520
+ "step": 510
521
+ },
522
+ {
523
+ "entropy": 0.8907318472862243,
524
+ "epoch": 0.44673539518900346,
525
+ "grad_norm": 0.19640029966831207,
526
+ "learning_rate": 0.0002331185567010309,
527
+ "loss": 0.8736312866210938,
528
+ "mean_token_accuracy": 0.7823721885681152,
529
+ "num_tokens": 1333535.0,
530
+ "step": 520
531
+ },
532
+ {
533
+ "entropy": 0.9001505076885223,
534
+ "epoch": 0.45532646048109965,
535
+ "grad_norm": 0.1615416407585144,
536
+ "learning_rate": 0.00023182989690721648,
537
+ "loss": 0.9240070343017578,
538
+ "mean_token_accuracy": 0.7720246851444245,
539
+ "num_tokens": 1359100.0,
540
+ "step": 530
541
+ },
542
+ {
543
+ "entropy": 0.8964834988117218,
544
+ "epoch": 0.4639175257731959,
545
+ "grad_norm": 0.16148629784584045,
546
+ "learning_rate": 0.00023054123711340206,
547
+ "loss": 0.9040334701538086,
548
+ "mean_token_accuracy": 0.7697900414466858,
549
+ "num_tokens": 1386642.0,
550
+ "step": 540
551
+ },
552
+ {
553
+ "entropy": 0.9152944028377533,
554
+ "epoch": 0.4725085910652921,
555
+ "grad_norm": 0.16060467064380646,
556
+ "learning_rate": 0.00022925257731958758,
557
+ "loss": 0.9094497680664062,
558
+ "mean_token_accuracy": 0.771862530708313,
559
+ "num_tokens": 1412712.0,
560
+ "step": 550
561
+ },
562
+ {
563
+ "entropy": 0.8443439364433288,
564
+ "epoch": 0.48109965635738833,
565
+ "grad_norm": 0.16139253973960876,
566
+ "learning_rate": 0.00022796391752577316,
567
+ "loss": 0.8445466041564942,
568
+ "mean_token_accuracy": 0.7862408220767975,
569
+ "num_tokens": 1439208.0,
570
+ "step": 560
571
+ },
572
+ {
573
+ "entropy": 0.8669894218444825,
574
+ "epoch": 0.4896907216494845,
575
+ "grad_norm": 0.13149064779281616,
576
+ "learning_rate": 0.00022667525773195873,
577
+ "loss": 0.8791823387145996,
578
+ "mean_token_accuracy": 0.7771342694759369,
579
+ "num_tokens": 1466602.0,
580
+ "step": 570
581
+ },
582
+ {
583
+ "entropy": 0.8713687837123871,
584
+ "epoch": 0.49828178694158076,
585
+ "grad_norm": 0.15979787707328796,
586
+ "learning_rate": 0.0002253865979381443,
587
+ "loss": 0.8594341278076172,
588
+ "mean_token_accuracy": 0.7810667157173157,
589
+ "num_tokens": 1493048.0,
590
+ "step": 580
591
+ },
592
+ {
593
+ "entropy": 0.9155345737934113,
594
+ "epoch": 0.506872852233677,
595
+ "grad_norm": 0.18420852720737457,
596
+ "learning_rate": 0.00022409793814432989,
597
+ "loss": 0.915378475189209,
598
+ "mean_token_accuracy": 0.7724271833896637,
599
+ "num_tokens": 1517512.0,
600
+ "step": 590
601
+ },
602
+ {
603
+ "entropy": 0.891716331243515,
604
+ "epoch": 0.5154639175257731,
605
+ "grad_norm": 0.1665642410516739,
606
+ "learning_rate": 0.00022280927835051546,
607
+ "loss": 0.8950610160827637,
608
+ "mean_token_accuracy": 0.777475905418396,
609
+ "num_tokens": 1542765.0,
610
+ "step": 600
611
+ },
612
+ {
613
+ "entropy": 0.9547060489654541,
614
+ "epoch": 0.5240549828178694,
615
+ "grad_norm": 0.19496814906597137,
616
+ "learning_rate": 0.000221520618556701,
617
+ "loss": 0.9572202682495117,
618
+ "mean_token_accuracy": 0.7599396347999573,
619
+ "num_tokens": 1570704.0,
620
+ "step": 610
621
+ },
622
+ {
623
+ "entropy": 0.894726449251175,
624
+ "epoch": 0.5326460481099656,
625
+ "grad_norm": 0.162724569439888,
626
+ "learning_rate": 0.00022023195876288656,
627
+ "loss": 0.8876501083374023,
628
+ "mean_token_accuracy": 0.7815112411975861,
629
+ "num_tokens": 1594675.0,
630
+ "step": 620
631
+ },
632
+ {
633
+ "entropy": 0.8966928541660308,
634
+ "epoch": 0.5412371134020618,
635
+ "grad_norm": 0.1820981204509735,
636
+ "learning_rate": 0.00021894329896907214,
637
+ "loss": 0.8992101669311523,
638
+ "mean_token_accuracy": 0.7774552226066589,
639
+ "num_tokens": 1621381.0,
640
+ "step": 630
641
+ },
642
+ {
643
+ "entropy": 0.8320938229560852,
644
+ "epoch": 0.5498281786941581,
645
+ "grad_norm": 0.18641315400600433,
646
+ "learning_rate": 0.0002176546391752577,
647
+ "loss": 0.8333114624023438,
648
+ "mean_token_accuracy": 0.7836415946483613,
649
+ "num_tokens": 1646589.0,
650
+ "step": 640
651
+ },
652
+ {
653
+ "entropy": 0.9350853085517883,
654
+ "epoch": 0.5584192439862543,
655
+ "grad_norm": 0.15154911577701569,
656
+ "learning_rate": 0.0002163659793814433,
657
+ "loss": 0.9300890922546386,
658
+ "mean_token_accuracy": 0.7711542308330536,
659
+ "num_tokens": 1672281.0,
660
+ "step": 650
661
+ },
662
+ {
663
+ "entropy": 0.8372068881988526,
664
+ "epoch": 0.5670103092783505,
665
+ "grad_norm": 0.157988041639328,
666
+ "learning_rate": 0.00021507731958762884,
667
+ "loss": 0.8490092277526855,
668
+ "mean_token_accuracy": 0.7828391373157502,
669
+ "num_tokens": 1697203.0,
670
+ "step": 660
671
+ },
672
+ {
673
+ "entropy": 0.8175156831741333,
674
+ "epoch": 0.5756013745704467,
675
+ "grad_norm": 0.16615159809589386,
676
+ "learning_rate": 0.0002137886597938144,
677
+ "loss": 0.7962069988250733,
678
+ "mean_token_accuracy": 0.793628740310669,
679
+ "num_tokens": 1722977.0,
680
+ "step": 670
681
+ },
682
+ {
683
+ "entropy": 0.9293979525566101,
684
+ "epoch": 0.584192439862543,
685
+ "grad_norm": 0.13969369232654572,
686
+ "learning_rate": 0.0002125,
687
+ "loss": 0.9364766120910645,
688
+ "mean_token_accuracy": 0.7693193078041076,
689
+ "num_tokens": 1748869.0,
690
+ "step": 680
691
+ },
692
+ {
693
+ "entropy": 0.8958774268627167,
694
+ "epoch": 0.5927835051546392,
695
+ "grad_norm": 0.14425267279148102,
696
+ "learning_rate": 0.00021121134020618556,
697
+ "loss": 0.8842126846313476,
698
+ "mean_token_accuracy": 0.7759437382221221,
699
+ "num_tokens": 1774715.0,
700
+ "step": 690
701
+ },
702
+ {
703
+ "entropy": 0.929528433084488,
704
+ "epoch": 0.6013745704467354,
705
+ "grad_norm": 0.15875831246376038,
706
+ "learning_rate": 0.0002099226804123711,
707
+ "loss": 0.9353519439697265,
708
+ "mean_token_accuracy": 0.769885140657425,
709
+ "num_tokens": 1800430.0,
710
+ "step": 700
711
+ },
712
+ {
713
+ "entropy": 0.8651838064193725,
714
+ "epoch": 0.6099656357388317,
715
+ "grad_norm": 0.19464287161827087,
716
+ "learning_rate": 0.00020863402061855666,
717
+ "loss": 0.8678315162658692,
718
+ "mean_token_accuracy": 0.7783544957637787,
719
+ "num_tokens": 1824631.0,
720
+ "step": 710
721
+ },
722
+ {
723
+ "entropy": 0.874721884727478,
724
+ "epoch": 0.6185567010309279,
725
+ "grad_norm": 0.17015859484672546,
726
+ "learning_rate": 0.00020734536082474224,
727
+ "loss": 0.847961139678955,
728
+ "mean_token_accuracy": 0.7813271641731262,
729
+ "num_tokens": 1849593.0,
730
+ "step": 720
731
+ },
732
+ {
733
+ "entropy": 0.8706691145896912,
734
+ "epoch": 0.627147766323024,
735
+ "grad_norm": 0.15960460901260376,
736
+ "learning_rate": 0.00020605670103092781,
737
+ "loss": 0.8743976593017578,
738
+ "mean_token_accuracy": 0.7772314369678497,
739
+ "num_tokens": 1874593.0,
740
+ "step": 730
741
+ },
742
+ {
743
+ "entropy": 0.824019831418991,
744
+ "epoch": 0.6357388316151202,
745
+ "grad_norm": 0.16192808747291565,
746
+ "learning_rate": 0.0002047680412371134,
747
+ "loss": 0.8214377403259278,
748
+ "mean_token_accuracy": 0.7877136409282685,
749
+ "num_tokens": 1899592.0,
750
+ "step": 740
751
+ },
752
+ {
753
+ "entropy": 0.853943508863449,
754
+ "epoch": 0.6443298969072165,
755
+ "grad_norm": 0.14331476390361786,
756
+ "learning_rate": 0.00020347938144329897,
757
+ "loss": 0.8542372703552246,
758
+ "mean_token_accuracy": 0.7825242638587951,
759
+ "num_tokens": 1925105.0,
760
+ "step": 750
761
+ },
762
+ {
763
+ "entropy": 0.9206405401229858,
764
+ "epoch": 0.6529209621993127,
765
+ "grad_norm": 0.16269327700138092,
766
+ "learning_rate": 0.00020219072164948454,
767
+ "loss": 0.9131902694702149,
768
+ "mean_token_accuracy": 0.7722071945667267,
769
+ "num_tokens": 1952126.0,
770
+ "step": 760
771
+ },
772
+ {
773
+ "entropy": 0.8438917219638824,
774
+ "epoch": 0.6615120274914089,
775
+ "grad_norm": 0.18783624470233917,
776
+ "learning_rate": 0.00020090206185567006,
777
+ "loss": 0.8612903594970703,
778
+ "mean_token_accuracy": 0.7805359125137329,
779
+ "num_tokens": 1976727.0,
780
+ "step": 770
781
+ },
782
+ {
783
+ "entropy": 0.8782703518867493,
784
+ "epoch": 0.6701030927835051,
785
+ "grad_norm": 0.1749051958322525,
786
+ "learning_rate": 0.00019961340206185564,
787
+ "loss": 0.8877654075622559,
788
+ "mean_token_accuracy": 0.775201940536499,
789
+ "num_tokens": 2004204.0,
790
+ "step": 780
791
+ },
792
+ {
793
+ "entropy": 0.9036802053451538,
794
+ "epoch": 0.6786941580756014,
795
+ "grad_norm": 0.17728140950202942,
796
+ "learning_rate": 0.00019832474226804121,
797
+ "loss": 0.8946940422058105,
798
+ "mean_token_accuracy": 0.7781059086322785,
799
+ "num_tokens": 2031926.0,
800
+ "step": 790
801
+ },
802
+ {
803
+ "entropy": 0.9057375133037567,
804
+ "epoch": 0.6872852233676976,
805
+ "grad_norm": 0.1804327517747879,
806
+ "learning_rate": 0.0001970360824742268,
807
+ "loss": 0.9067662239074707,
808
+ "mean_token_accuracy": 0.7786577641963959,
809
+ "num_tokens": 2059628.0,
810
+ "step": 800
811
+ },
812
+ {
813
+ "entropy": 0.8421910405158997,
814
+ "epoch": 0.6958762886597938,
815
+ "grad_norm": 0.1570490598678589,
816
+ "learning_rate": 0.00019574742268041237,
817
+ "loss": 0.8346380233764649,
818
+ "mean_token_accuracy": 0.7839872419834137,
819
+ "num_tokens": 2083943.0,
820
+ "step": 810
821
+ },
822
+ {
823
+ "entropy": 0.8743102788925171,
824
+ "epoch": 0.7044673539518901,
825
+ "grad_norm": 0.1737687736749649,
826
+ "learning_rate": 0.00019445876288659794,
827
+ "loss": 0.8622690200805664,
828
+ "mean_token_accuracy": 0.7791435241699218,
829
+ "num_tokens": 2109444.0,
830
+ "step": 820
831
+ },
832
+ {
833
+ "entropy": 0.8926449298858643,
834
+ "epoch": 0.7130584192439863,
835
+ "grad_norm": 0.1972922682762146,
836
+ "learning_rate": 0.0001931701030927835,
837
+ "loss": 0.895075511932373,
838
+ "mean_token_accuracy": 0.7716107368469238,
839
+ "num_tokens": 2134209.0,
840
+ "step": 830
841
+ },
842
+ {
843
+ "entropy": 0.8712981760501861,
844
+ "epoch": 0.7216494845360825,
845
+ "grad_norm": 0.16580969095230103,
846
+ "learning_rate": 0.00019188144329896904,
847
+ "loss": 0.8615394592285156,
848
+ "mean_token_accuracy": 0.780440878868103,
849
+ "num_tokens": 2160322.0,
850
+ "step": 840
851
+ },
852
+ {
853
+ "entropy": 0.7872600853443146,
854
+ "epoch": 0.7302405498281787,
855
+ "grad_norm": 0.17089906334877014,
856
+ "learning_rate": 0.00019059278350515462,
857
+ "loss": 0.7738356590270996,
858
+ "mean_token_accuracy": 0.7984008610248565,
859
+ "num_tokens": 2186673.0,
860
+ "step": 850
861
+ },
862
+ {
863
+ "entropy": 0.8024394273757934,
864
+ "epoch": 0.738831615120275,
865
+ "grad_norm": 0.15015770494937897,
866
+ "learning_rate": 0.0001893041237113402,
867
+ "loss": 0.801088809967041,
868
+ "mean_token_accuracy": 0.7919513285160065,
869
+ "num_tokens": 2211288.0,
870
+ "step": 860
871
+ },
872
+ {
873
+ "entropy": 0.8289069414138794,
874
+ "epoch": 0.7474226804123711,
875
+ "grad_norm": 0.15804092586040497,
876
+ "learning_rate": 0.00018801546391752577,
877
+ "loss": 0.8228999137878418,
878
+ "mean_token_accuracy": 0.7900383174419403,
879
+ "num_tokens": 2235494.0,
880
+ "step": 870
881
+ },
882
+ {
883
+ "entropy": 0.8337613046169281,
884
+ "epoch": 0.7560137457044673,
885
+ "grad_norm": 0.16564202308654785,
886
+ "learning_rate": 0.00018672680412371132,
887
+ "loss": 0.8357515335083008,
888
+ "mean_token_accuracy": 0.7796721041202546,
889
+ "num_tokens": 2261853.0,
890
+ "step": 880
891
+ },
892
+ {
893
+ "entropy": 0.8514188408851624,
894
+ "epoch": 0.7646048109965635,
895
+ "grad_norm": 0.15763898193836212,
896
+ "learning_rate": 0.0001854381443298969,
897
+ "loss": 0.8596377372741699,
898
+ "mean_token_accuracy": 0.781908392906189,
899
+ "num_tokens": 2288235.0,
900
+ "step": 890
901
+ },
902
+ {
903
+ "entropy": 0.8182059168815613,
904
+ "epoch": 0.7731958762886598,
905
+ "grad_norm": 0.14843082427978516,
906
+ "learning_rate": 0.00018414948453608247,
907
+ "loss": 0.8056727409362793,
908
+ "mean_token_accuracy": 0.7887085616588593,
909
+ "num_tokens": 2312636.0,
910
+ "step": 900
911
+ },
912
+ {
913
+ "entropy": 0.8019815027713776,
914
+ "epoch": 0.781786941580756,
915
+ "grad_norm": 0.1444498896598816,
916
+ "learning_rate": 0.00018286082474226804,
917
+ "loss": 0.7791801452636719,
918
+ "mean_token_accuracy": 0.7921311616897583,
919
+ "num_tokens": 2337590.0,
920
+ "step": 910
921
+ },
922
+ {
923
+ "entropy": 0.8811851978302002,
924
+ "epoch": 0.7903780068728522,
925
+ "grad_norm": 0.20295798778533936,
926
+ "learning_rate": 0.0001815721649484536,
927
+ "loss": 0.9093887329101562,
928
+ "mean_token_accuracy": 0.7765615165233613,
929
+ "num_tokens": 2364733.0,
930
+ "step": 920
931
+ },
932
+ {
933
+ "entropy": 0.8845545649528503,
934
+ "epoch": 0.7989690721649485,
935
+ "grad_norm": 0.16211840510368347,
936
+ "learning_rate": 0.00018028350515463914,
937
+ "loss": 0.87130765914917,
938
+ "mean_token_accuracy": 0.773591297864914,
939
+ "num_tokens": 2389138.0,
940
+ "step": 930
941
+ },
942
+ {
943
+ "entropy": 0.8366316437721253,
944
+ "epoch": 0.8075601374570447,
945
+ "grad_norm": 0.1796175092458725,
946
+ "learning_rate": 0.00017899484536082472,
947
+ "loss": 0.8393503189086914,
948
+ "mean_token_accuracy": 0.7834821224212647,
949
+ "num_tokens": 2416288.0,
950
+ "step": 940
951
+ },
952
+ {
953
+ "entropy": 0.8687749207019806,
954
+ "epoch": 0.8161512027491409,
955
+ "grad_norm": 0.17602433264255524,
956
+ "learning_rate": 0.0001777061855670103,
957
+ "loss": 0.8831927299499511,
958
+ "mean_token_accuracy": 0.7806714773178101,
959
+ "num_tokens": 2442282.0,
960
+ "step": 950
961
+ },
962
+ {
963
+ "entropy": 0.8602447748184204,
964
+ "epoch": 0.8247422680412371,
965
+ "grad_norm": 0.14779216051101685,
966
+ "learning_rate": 0.00017641752577319587,
967
+ "loss": 0.8557798385620117,
968
+ "mean_token_accuracy": 0.7800233542919159,
969
+ "num_tokens": 2469865.0,
970
+ "step": 960
971
+ },
972
+ {
973
+ "entropy": 0.8987692832946778,
974
+ "epoch": 0.8333333333333334,
975
+ "grad_norm": 0.16065306961536407,
976
+ "learning_rate": 0.00017512886597938145,
977
+ "loss": 0.8930519104003907,
978
+ "mean_token_accuracy": 0.7771966874599456,
979
+ "num_tokens": 2497716.0,
980
+ "step": 970
981
+ },
982
+ {
983
+ "entropy": 0.847240948677063,
984
+ "epoch": 0.8419243986254296,
985
+ "grad_norm": 0.16063280403614044,
986
+ "learning_rate": 0.00017384020618556702,
987
+ "loss": 0.8212727546691895,
988
+ "mean_token_accuracy": 0.7900629937648773,
989
+ "num_tokens": 2523292.0,
990
+ "step": 980
991
+ },
992
+ {
993
+ "entropy": 0.8685175359249115,
994
+ "epoch": 0.8505154639175257,
995
+ "grad_norm": 0.19293011724948883,
996
+ "learning_rate": 0.00017255154639175254,
997
+ "loss": 0.8839738845825196,
998
+ "mean_token_accuracy": 0.7780547857284545,
999
+ "num_tokens": 2547529.0,
1000
+ "step": 990
1001
+ },
1002
+ {
1003
+ "entropy": 0.8589172720909118,
1004
+ "epoch": 0.8591065292096219,
1005
+ "grad_norm": 0.13081640005111694,
1006
+ "learning_rate": 0.00017126288659793812,
1007
+ "loss": 0.8760468482971191,
1008
+ "mean_token_accuracy": 0.7810062050819397,
1009
+ "num_tokens": 2574943.0,
1010
+ "step": 1000
1011
+ },
1012
+ {
1013
+ "entropy": 0.8676146745681763,
1014
+ "epoch": 0.8676975945017182,
1015
+ "grad_norm": 0.18423762917518616,
1016
+ "learning_rate": 0.0001699742268041237,
1017
+ "loss": 0.8576597213745117,
1018
+ "mean_token_accuracy": 0.7845786452293396,
1019
+ "num_tokens": 2599770.0,
1020
+ "step": 1010
1021
+ },
1022
+ {
1023
+ "entropy": 0.8578267991542816,
1024
+ "epoch": 0.8762886597938144,
1025
+ "grad_norm": 0.15971733629703522,
1026
+ "learning_rate": 0.00016868556701030927,
1027
+ "loss": 0.8597639083862305,
1028
+ "mean_token_accuracy": 0.7836935818195343,
1029
+ "num_tokens": 2629050.0,
1030
+ "step": 1020
1031
+ },
1032
+ {
1033
+ "entropy": 0.78883256316185,
1034
+ "epoch": 0.8848797250859106,
1035
+ "grad_norm": 0.19334255158901215,
1036
+ "learning_rate": 0.00016739690721649485,
1037
+ "loss": 0.775438642501831,
1038
+ "mean_token_accuracy": 0.7994126856327057,
1039
+ "num_tokens": 2652418.0,
1040
+ "step": 1030
1041
+ },
1042
+ {
1043
+ "entropy": 0.827907818555832,
1044
+ "epoch": 0.8934707903780069,
1045
+ "grad_norm": 0.17899778485298157,
1046
+ "learning_rate": 0.0001661082474226804,
1047
+ "loss": 0.8155632019042969,
1048
+ "mean_token_accuracy": 0.7867587625980377,
1049
+ "num_tokens": 2677020.0,
1050
+ "step": 1040
1051
+ },
1052
+ {
1053
+ "entropy": 0.8488841831684113,
1054
+ "epoch": 0.9020618556701031,
1055
+ "grad_norm": 0.17594760656356812,
1056
+ "learning_rate": 0.00016481958762886597,
1057
+ "loss": 0.8443450927734375,
1058
+ "mean_token_accuracy": 0.7823632657527924,
1059
+ "num_tokens": 2700589.0,
1060
+ "step": 1050
1061
+ },
1062
+ {
1063
+ "entropy": 0.9077254295349121,
1064
+ "epoch": 0.9106529209621993,
1065
+ "grad_norm": 0.17107197642326355,
1066
+ "learning_rate": 0.00016353092783505152,
1067
+ "loss": 0.9043283462524414,
1068
+ "mean_token_accuracy": 0.7685754477977753,
1069
+ "num_tokens": 2727925.0,
1070
+ "step": 1060
1071
+ },
1072
+ {
1073
+ "entropy": 0.8681511282920837,
1074
+ "epoch": 0.9192439862542955,
1075
+ "grad_norm": 0.16261200606822968,
1076
+ "learning_rate": 0.0001622422680412371,
1077
+ "loss": 0.8636059761047363,
1078
+ "mean_token_accuracy": 0.7801066517829895,
1079
+ "num_tokens": 2754434.0,
1080
+ "step": 1070
1081
+ },
1082
+ {
1083
+ "entropy": 0.8201885402202607,
1084
+ "epoch": 0.9278350515463918,
1085
+ "grad_norm": 0.15494069457054138,
1086
+ "learning_rate": 0.00016095360824742267,
1087
+ "loss": 0.8267613410949707,
1088
+ "mean_token_accuracy": 0.7909399390220642,
1089
+ "num_tokens": 2778856.0,
1090
+ "step": 1080
1091
+ },
1092
+ {
1093
+ "entropy": 0.8281429171562195,
1094
+ "epoch": 0.936426116838488,
1095
+ "grad_norm": 0.15491366386413574,
1096
+ "learning_rate": 0.00015966494845360822,
1097
+ "loss": 0.8283931732177734,
1098
+ "mean_token_accuracy": 0.7858192563056946,
1099
+ "num_tokens": 2802467.0,
1100
+ "step": 1090
1101
+ },
1102
+ {
1103
+ "entropy": 0.8585192024707794,
1104
+ "epoch": 0.9450171821305842,
1105
+ "grad_norm": 0.16744765639305115,
1106
+ "learning_rate": 0.0001583762886597938,
1107
+ "loss": 0.8450031280517578,
1108
+ "mean_token_accuracy": 0.7824375092983246,
1109
+ "num_tokens": 2828404.0,
1110
+ "step": 1100
1111
+ },
1112
+ {
1113
+ "entropy": 0.9703075528144837,
1114
+ "epoch": 0.9536082474226805,
1115
+ "grad_norm": 0.16334781050682068,
1116
+ "learning_rate": 0.00015708762886597937,
1117
+ "loss": 0.9652547836303711,
1118
+ "mean_token_accuracy": 0.7639409124851226,
1119
+ "num_tokens": 2854621.0,
1120
+ "step": 1110
1121
+ },
1122
+ {
1123
+ "entropy": 0.8723820388317108,
1124
+ "epoch": 0.9621993127147767,
1125
+ "grad_norm": 0.15771399438381195,
1126
+ "learning_rate": 0.00015579896907216495,
1127
+ "loss": 0.8663213729858399,
1128
+ "mean_token_accuracy": 0.7808757722377777,
1129
+ "num_tokens": 2880448.0,
1130
+ "step": 1120
1131
+ },
1132
+ {
1133
+ "entropy": 0.8375514090061188,
1134
+ "epoch": 0.9707903780068728,
1135
+ "grad_norm": 0.16101613640785217,
1136
+ "learning_rate": 0.0001545103092783505,
1137
+ "loss": 0.8438360214233398,
1138
+ "mean_token_accuracy": 0.7864978194236756,
1139
+ "num_tokens": 2907346.0,
1140
+ "step": 1130
1141
+ },
1142
+ {
1143
+ "entropy": 0.836071914434433,
1144
+ "epoch": 0.979381443298969,
1145
+ "grad_norm": 0.19959381222724915,
1146
+ "learning_rate": 0.00015322164948453605,
1147
+ "loss": 0.8347750663757324,
1148
+ "mean_token_accuracy": 0.7878862261772156,
1149
+ "num_tokens": 2932769.0,
1150
+ "step": 1140
1151
+ },
1152
+ {
1153
+ "entropy": 0.8218921303749085,
1154
+ "epoch": 0.9879725085910653,
1155
+ "grad_norm": 0.17999783158302307,
1156
+ "learning_rate": 0.00015193298969072162,
1157
+ "loss": 0.81912841796875,
1158
+ "mean_token_accuracy": 0.7904701411724091,
1159
+ "num_tokens": 2956439.0,
1160
+ "step": 1150
1161
+ },
1162
+ {
1163
+ "entropy": 0.8836544632911683,
1164
+ "epoch": 0.9965635738831615,
1165
+ "grad_norm": 0.1764528602361679,
1166
+ "learning_rate": 0.0001506443298969072,
1167
+ "loss": 0.8860954284667969,
1168
+ "mean_token_accuracy": 0.7801528871059418,
1169
+ "num_tokens": 2983309.0,
1170
+ "step": 1160
1171
+ },
1172
+ {
1173
+ "entropy": 0.8893479645252228,
1174
+ "epoch": 1.0051546391752577,
1175
+ "grad_norm": 0.153923898935318,
1176
+ "learning_rate": 0.00014935567010309277,
1177
+ "loss": 0.8747451782226563,
1178
+ "mean_token_accuracy": 0.7765235006809235,
1179
+ "num_tokens": 3010175.0,
1180
+ "step": 1170
1181
+ },
1182
+ {
1183
+ "entropy": 0.8393925905227662,
1184
+ "epoch": 1.013745704467354,
1185
+ "grad_norm": 0.16801179945468903,
1186
+ "learning_rate": 0.00014806701030927835,
1187
+ "loss": 0.8316682815551758,
1188
+ "mean_token_accuracy": 0.7851088464260101,
1189
+ "num_tokens": 3036705.0,
1190
+ "step": 1180
1191
+ },
1192
+ {
1193
+ "entropy": 0.8521030902862549,
1194
+ "epoch": 1.02233676975945,
1195
+ "grad_norm": 0.16726894676685333,
1196
+ "learning_rate": 0.0001467783505154639,
1197
+ "loss": 0.8441553115844727,
1198
+ "mean_token_accuracy": 0.7883111536502838,
1199
+ "num_tokens": 3062184.0,
1200
+ "step": 1190
1201
+ },
1202
+ {
1203
+ "entropy": 0.8722131133079529,
1204
+ "epoch": 1.0309278350515463,
1205
+ "grad_norm": 0.1777733713388443,
1206
+ "learning_rate": 0.00014548969072164948,
1207
+ "loss": 0.8671235084533692,
1208
+ "mean_token_accuracy": 0.7776070535182953,
1209
+ "num_tokens": 3089854.0,
1210
+ "step": 1200
1211
+ },
1212
+ {
1213
+ "entropy": 0.8229423344135285,
1214
+ "epoch": 1.0395189003436427,
1215
+ "grad_norm": 0.13976716995239258,
1216
+ "learning_rate": 0.00014420103092783505,
1217
+ "loss": 0.8249235153198242,
1218
+ "mean_token_accuracy": 0.7889478981494904,
1219
+ "num_tokens": 3116938.0,
1220
+ "step": 1210
1221
+ },
1222
+ {
1223
+ "entropy": 0.8179661929607391,
1224
+ "epoch": 1.0481099656357389,
1225
+ "grad_norm": 0.16781210899353027,
1226
+ "learning_rate": 0.0001429123711340206,
1227
+ "loss": 0.8102549552917481,
1228
+ "mean_token_accuracy": 0.7927488446235657,
1229
+ "num_tokens": 3143825.0,
1230
+ "step": 1220
1231
+ },
1232
+ {
1233
+ "entropy": 0.7997653067111969,
1234
+ "epoch": 1.056701030927835,
1235
+ "grad_norm": 0.1647312045097351,
1236
+ "learning_rate": 0.00014162371134020618,
1237
+ "loss": 0.7900454998016357,
1238
+ "mean_token_accuracy": 0.7910003781318664,
1239
+ "num_tokens": 3168362.0,
1240
+ "step": 1230
1241
+ },
1242
+ {
1243
+ "entropy": 0.8539471685886383,
1244
+ "epoch": 1.0652920962199313,
1245
+ "grad_norm": 0.16146644949913025,
1246
+ "learning_rate": 0.00014033505154639175,
1247
+ "loss": 0.8489527702331543,
1248
+ "mean_token_accuracy": 0.7828418910503387,
1249
+ "num_tokens": 3192789.0,
1250
+ "step": 1240
1251
+ },
1252
+ {
1253
+ "entropy": 0.7987524449825287,
1254
+ "epoch": 1.0738831615120275,
1255
+ "grad_norm": 0.16997887194156647,
1256
+ "learning_rate": 0.0001390463917525773,
1257
+ "loss": 0.7815001964569092,
1258
+ "mean_token_accuracy": 0.7958592414855957,
1259
+ "num_tokens": 3218189.0,
1260
+ "step": 1250
1261
+ },
1262
+ {
1263
+ "entropy": 0.8433911919593811,
1264
+ "epoch": 1.0824742268041236,
1265
+ "grad_norm": 0.20293352007865906,
1266
+ "learning_rate": 0.00013775773195876288,
1267
+ "loss": 0.8536633491516114,
1268
+ "mean_token_accuracy": 0.7847901046276092,
1269
+ "num_tokens": 3242357.0,
1270
+ "step": 1260
1271
+ },
1272
+ {
1273
+ "entropy": 0.7989895343780518,
1274
+ "epoch": 1.0910652920962198,
1275
+ "grad_norm": 0.17577318847179413,
1276
+ "learning_rate": 0.00013646907216494845,
1277
+ "loss": 0.795603084564209,
1278
+ "mean_token_accuracy": 0.7937948822975158,
1279
+ "num_tokens": 3266634.0,
1280
+ "step": 1270
1281
+ },
1282
+ {
1283
+ "entropy": 0.849328339099884,
1284
+ "epoch": 1.0996563573883162,
1285
+ "grad_norm": 0.16344796121120453,
1286
+ "learning_rate": 0.000135180412371134,
1287
+ "loss": 0.8569498062133789,
1288
+ "mean_token_accuracy": 0.784570574760437,
1289
+ "num_tokens": 3292457.0,
1290
+ "step": 1280
1291
+ },
1292
+ {
1293
+ "entropy": 0.9115039288997651,
1294
+ "epoch": 1.1082474226804124,
1295
+ "grad_norm": 0.16120180487632751,
1296
+ "learning_rate": 0.00013389175257731958,
1297
+ "loss": 0.9172770500183105,
1298
+ "mean_token_accuracy": 0.7738337874412536,
1299
+ "num_tokens": 3319975.0,
1300
+ "step": 1290
1301
+ },
1302
+ {
1303
+ "entropy": 0.9116923689842225,
1304
+ "epoch": 1.1168384879725086,
1305
+ "grad_norm": 0.1507127285003662,
1306
+ "learning_rate": 0.00013260309278350513,
1307
+ "loss": 0.9115254402160644,
1308
+ "mean_token_accuracy": 0.7727989673614502,
1309
+ "num_tokens": 3349370.0,
1310
+ "step": 1300
1311
+ },
1312
+ {
1313
+ "entropy": 0.8705029428005219,
1314
+ "epoch": 1.1254295532646048,
1315
+ "grad_norm": 0.16798697412014008,
1316
+ "learning_rate": 0.0001313144329896907,
1317
+ "loss": 0.8673007011413574,
1318
+ "mean_token_accuracy": 0.7829907596111297,
1319
+ "num_tokens": 3376086.0,
1320
+ "step": 1310
1321
+ },
1322
+ {
1323
+ "entropy": 0.854372763633728,
1324
+ "epoch": 1.134020618556701,
1325
+ "grad_norm": 0.17355380952358246,
1326
+ "learning_rate": 0.00013002577319587628,
1327
+ "loss": 0.8472127914428711,
1328
+ "mean_token_accuracy": 0.783273845911026,
1329
+ "num_tokens": 3402983.0,
1330
+ "step": 1320
1331
+ },
1332
+ {
1333
+ "entropy": 0.8452303171157837,
1334
+ "epoch": 1.1426116838487972,
1335
+ "grad_norm": 0.17868086695671082,
1336
+ "learning_rate": 0.00012873711340206183,
1337
+ "loss": 0.846036434173584,
1338
+ "mean_token_accuracy": 0.781141072511673,
1339
+ "num_tokens": 3430354.0,
1340
+ "step": 1330
1341
+ },
1342
+ {
1343
+ "entropy": 0.8574681997299194,
1344
+ "epoch": 1.1512027491408934,
1345
+ "grad_norm": 0.17731839418411255,
1346
+ "learning_rate": 0.0001274484536082474,
1347
+ "loss": 0.8377547264099121,
1348
+ "mean_token_accuracy": 0.7869145214557648,
1349
+ "num_tokens": 3456008.0,
1350
+ "step": 1340
1351
+ },
1352
+ {
1353
+ "entropy": 0.8843120455741882,
1354
+ "epoch": 1.1597938144329896,
1355
+ "grad_norm": 0.17710870504379272,
1356
+ "learning_rate": 0.00012615979381443298,
1357
+ "loss": 0.894843578338623,
1358
+ "mean_token_accuracy": 0.7717236757278443,
1359
+ "num_tokens": 3481183.0,
1360
+ "step": 1350
1361
+ },
1362
+ {
1363
+ "entropy": 0.82523393034935,
1364
+ "epoch": 1.168384879725086,
1365
+ "grad_norm": 0.1979239135980606,
1366
+ "learning_rate": 0.00012487113402061855,
1367
+ "loss": 0.8107866287231446,
1368
+ "mean_token_accuracy": 0.7911741256713867,
1369
+ "num_tokens": 3508453.0,
1370
+ "step": 1360
1371
+ },
1372
+ {
1373
+ "entropy": 0.7964858949184418,
1374
+ "epoch": 1.1769759450171822,
1375
+ "grad_norm": 0.14254362881183624,
1376
+ "learning_rate": 0.0001235824742268041,
1377
+ "loss": 0.7863273620605469,
1378
+ "mean_token_accuracy": 0.7948981881141662,
1379
+ "num_tokens": 3534922.0,
1380
+ "step": 1370
1381
+ },
1382
+ {
1383
+ "entropy": 0.8244574010372162,
1384
+ "epoch": 1.1855670103092784,
1385
+ "grad_norm": 0.17570418119430542,
1386
+ "learning_rate": 0.00012229381443298968,
1387
+ "loss": 0.8179922103881836,
1388
+ "mean_token_accuracy": 0.7896173536777497,
1389
+ "num_tokens": 3559367.0,
1390
+ "step": 1380
1391
+ },
1392
+ {
1393
+ "entropy": 0.8134994566440582,
1394
+ "epoch": 1.1941580756013745,
1395
+ "grad_norm": 0.1930961012840271,
1396
+ "learning_rate": 0.00012100515463917526,
1397
+ "loss": 0.8178740501403808,
1398
+ "mean_token_accuracy": 0.7915168821811676,
1399
+ "num_tokens": 3585435.0,
1400
+ "step": 1390
1401
+ },
1402
+ {
1403
+ "entropy": 0.8384514689445496,
1404
+ "epoch": 1.2027491408934707,
1405
+ "grad_norm": 0.17272983491420746,
1406
+ "learning_rate": 0.00011971649484536082,
1407
+ "loss": 0.8454058647155762,
1408
+ "mean_token_accuracy": 0.7868218302726746,
1409
+ "num_tokens": 3613232.0,
1410
+ "step": 1400
1411
+ },
1412
+ {
1413
+ "entropy": 0.9280701756477356,
1414
+ "epoch": 1.211340206185567,
1415
+ "grad_norm": 0.1684003472328186,
1416
+ "learning_rate": 0.00011842783505154638,
1417
+ "loss": 0.9219778060913086,
1418
+ "mean_token_accuracy": 0.7722154259681702,
1419
+ "num_tokens": 3641457.0,
1420
+ "step": 1410
1421
+ },
1422
+ {
1423
+ "entropy": 0.832447475194931,
1424
+ "epoch": 1.2199312714776633,
1425
+ "grad_norm": 0.1773725152015686,
1426
+ "learning_rate": 0.00011713917525773194,
1427
+ "loss": 0.8198453903198242,
1428
+ "mean_token_accuracy": 0.7914207100868225,
1429
+ "num_tokens": 3666482.0,
1430
+ "step": 1420
1431
+ },
1432
+ {
1433
+ "entropy": 0.8738070428371429,
1434
+ "epoch": 1.2285223367697595,
1435
+ "grad_norm": 0.18506592512130737,
1436
+ "learning_rate": 0.00011585051546391752,
1437
+ "loss": 0.8723980903625488,
1438
+ "mean_token_accuracy": 0.778689730167389,
1439
+ "num_tokens": 3692349.0,
1440
+ "step": 1430
1441
+ },
1442
+ {
1443
+ "entropy": 0.8338236033916473,
1444
+ "epoch": 1.2371134020618557,
1445
+ "grad_norm": 0.17101170122623444,
1446
+ "learning_rate": 0.00011456185567010308,
1447
+ "loss": 0.837257194519043,
1448
+ "mean_token_accuracy": 0.7857484579086303,
1449
+ "num_tokens": 3717280.0,
1450
+ "step": 1440
1451
+ },
1452
+ {
1453
+ "entropy": 0.8157192528247833,
1454
+ "epoch": 1.245704467353952,
1455
+ "grad_norm": 0.15433582663536072,
1456
+ "learning_rate": 0.00011327319587628864,
1457
+ "loss": 0.8053474426269531,
1458
+ "mean_token_accuracy": 0.7934693098068237,
1459
+ "num_tokens": 3742595.0,
1460
+ "step": 1450
1461
+ },
1462
+ {
1463
+ "entropy": 0.8770197212696076,
1464
+ "epoch": 1.254295532646048,
1465
+ "grad_norm": 0.17446650564670563,
1466
+ "learning_rate": 0.00011198453608247422,
1467
+ "loss": 0.880764389038086,
1468
+ "mean_token_accuracy": 0.7784990787506103,
1469
+ "num_tokens": 3767913.0,
1470
+ "step": 1460
1471
+ },
1472
+ {
1473
+ "entropy": 0.8850863575935364,
1474
+ "epoch": 1.2628865979381443,
1475
+ "grad_norm": 0.186509370803833,
1476
+ "learning_rate": 0.0001106958762886598,
1477
+ "loss": 0.870394229888916,
1478
+ "mean_token_accuracy": 0.7804449439048767,
1479
+ "num_tokens": 3792206.0,
1480
+ "step": 1470
1481
+ },
1482
+ {
1483
+ "entropy": 0.8432760655879974,
1484
+ "epoch": 1.2714776632302405,
1485
+ "grad_norm": 0.17969489097595215,
1486
+ "learning_rate": 0.00010940721649484534,
1487
+ "loss": 0.8241963386535645,
1488
+ "mean_token_accuracy": 0.7888677537441253,
1489
+ "num_tokens": 3818423.0,
1490
+ "step": 1480
1491
+ },
1492
+ {
1493
+ "entropy": 0.8146856188774109,
1494
+ "epoch": 1.2800687285223367,
1495
+ "grad_norm": 0.15870600938796997,
1496
+ "learning_rate": 0.00010811855670103092,
1497
+ "loss": 0.8046764373779297,
1498
+ "mean_token_accuracy": 0.7905527114868164,
1499
+ "num_tokens": 3844217.0,
1500
+ "step": 1490
1501
+ },
1502
+ {
1503
+ "entropy": 0.8613445937633515,
1504
+ "epoch": 1.2886597938144329,
1505
+ "grad_norm": 0.16997215151786804,
1506
+ "learning_rate": 0.00010682989690721648,
1507
+ "loss": 0.8584735870361329,
1508
+ "mean_token_accuracy": 0.7848546147346497,
1509
+ "num_tokens": 3873420.0,
1510
+ "step": 1500
1511
+ }
1512
+ ],
1513
+ "logging_steps": 10,
1514
+ "max_steps": 2328,
1515
+ "num_input_tokens_seen": 0,
1516
+ "num_train_epochs": 2,
1517
+ "save_steps": 500,
1518
+ "stateful_callbacks": {
1519
+ "TrainerControl": {
1520
+ "args": {
1521
+ "should_epoch_stop": false,
1522
+ "should_evaluate": false,
1523
+ "should_log": false,
1524
+ "should_save": true,
1525
+ "should_training_stop": false
1526
+ },
1527
+ "attributes": {}
1528
+ }
1529
+ },
1530
+ "total_flos": 6335948395777536.0,
1531
+ "train_batch_size": 16,
1532
+ "trial_name": null,
1533
+ "trial_params": null
1534
+ }
checkpoint-1500/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f2af45870b4c4d611381846df6bdd4a32cb292a7b5897b79b7e5f196be57c596
3
+ size 5585
checkpoint-2000/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: HuggingFaceTB/SmolLM2-135M-Instruct
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.18.1
checkpoint-2000/adapter_config.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "HuggingFaceTB/SmolLM2-135M-Instruct",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.05,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "peft_version": "0.18.1",
27
+ "qalora_group_size": 16,
28
+ "r": 16,
29
+ "rank_pattern": {},
30
+ "revision": null,
31
+ "target_modules": [
32
+ "o_proj",
33
+ "k_proj",
34
+ "q_proj",
35
+ "v_proj"
36
+ ],
37
+ "target_parameters": null,
38
+ "task_type": "CAUSAL_LM",
39
+ "trainable_token_indices": null,
40
+ "use_dora": false,
41
+ "use_qalora": false,
42
+ "use_rslora": false
43
+ }
checkpoint-2000/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e5eefccd5f12a1c3dd9a421aefffc3b03d29ca7dde1c282e81d29b1d63dbd5d3
3
+ size 7404368
checkpoint-2000/chat_template.jinja ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
2
+ You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
3
+ ' }}{% endif %}{{'<|im_start|>' + message['role'] + '
4
+ ' + message['content'] + '<|im_end|>' + '
5
+ '}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
6
+ ' }}{% endif %}
checkpoint-2000/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f7f2a40c452403b39aa69d0053c8711ea9445d69827debf5e6ff85a59d0ead36
3
+ size 14950667
checkpoint-2000/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9c69f08e7e7238f8f16c24ef86292d12caa0a4ae096db194a9f2f78781144bdb
3
+ size 14645
checkpoint-2000/scaler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f4aa03f6e0cd07cf67ce1fbe3101d545f5771ef9148b9debf02b11cf6948da5c
3
+ size 1383
checkpoint-2000/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cfbe1f4fd21e5230551934a2e47447c4edbeafac827bb210ae27474717167e0b
3
+ size 1465
checkpoint-2000/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-2000/tokenizer_config.json ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": "<|im_start|>",
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|im_end|>",
7
+ "extra_special_tokens": [
8
+ "<|im_start|>",
9
+ "<|im_end|>"
10
+ ],
11
+ "is_local": false,
12
+ "model_max_length": 8192,
13
+ "pad_token": "<|im_end|>",
14
+ "tokenizer_class": "TokenizersBackend",
15
+ "unk_token": "<|endoftext|>",
16
+ "vocab_size": 49152
17
+ }
checkpoint-2000/trainer_state.json ADDED
@@ -0,0 +1,2034 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_global_step": null,
3
+ "best_metric": null,
4
+ "best_model_checkpoint": null,
5
+ "epoch": 1.718213058419244,
6
+ "eval_steps": 500,
7
+ "global_step": 2000,
8
+ "is_hyper_param_search": false,
9
+ "is_local_process_zero": true,
10
+ "is_world_process_zero": true,
11
+ "log_history": [
12
+ {
13
+ "entropy": 1.1566081464290618,
14
+ "epoch": 0.00859106529209622,
15
+ "grad_norm": 0.15214066207408905,
16
+ "learning_rate": 0.00029884020618556697,
17
+ "loss": 1.226055908203125,
18
+ "mean_token_accuracy": 0.7206311166286469,
19
+ "num_tokens": 26541.0,
20
+ "step": 10
21
+ },
22
+ {
23
+ "entropy": 1.2002840995788575,
24
+ "epoch": 0.01718213058419244,
25
+ "grad_norm": 0.1833968460559845,
26
+ "learning_rate": 0.00029755154639175255,
27
+ "loss": 1.2007878303527832,
28
+ "mean_token_accuracy": 0.7304173231124877,
29
+ "num_tokens": 52280.0,
30
+ "step": 20
31
+ },
32
+ {
33
+ "entropy": 1.083837980031967,
34
+ "epoch": 0.02577319587628866,
35
+ "grad_norm": 0.15183115005493164,
36
+ "learning_rate": 0.0002962628865979381,
37
+ "loss": 1.1582960128784179,
38
+ "mean_token_accuracy": 0.7341346919536591,
39
+ "num_tokens": 79603.0,
40
+ "step": 30
41
+ },
42
+ {
43
+ "entropy": 0.9830838143825531,
44
+ "epoch": 0.03436426116838488,
45
+ "grad_norm": 0.1640024185180664,
46
+ "learning_rate": 0.00029497422680412364,
47
+ "loss": 0.989743423461914,
48
+ "mean_token_accuracy": 0.7652481079101563,
49
+ "num_tokens": 106301.0,
50
+ "step": 40
51
+ },
52
+ {
53
+ "entropy": 0.9237875938415527,
54
+ "epoch": 0.0429553264604811,
55
+ "grad_norm": 0.1496010422706604,
56
+ "learning_rate": 0.0002936855670103092,
57
+ "loss": 0.9489053726196289,
58
+ "mean_token_accuracy": 0.7682253539562225,
59
+ "num_tokens": 131791.0,
60
+ "step": 50
61
+ },
62
+ {
63
+ "entropy": 0.945674192905426,
64
+ "epoch": 0.05154639175257732,
65
+ "grad_norm": 0.16326577961444855,
66
+ "learning_rate": 0.0002923969072164948,
67
+ "loss": 0.9549709320068359,
68
+ "mean_token_accuracy": 0.7691607773303986,
69
+ "num_tokens": 156376.0,
70
+ "step": 60
71
+ },
72
+ {
73
+ "entropy": 0.9946551442146301,
74
+ "epoch": 0.06013745704467354,
75
+ "grad_norm": 0.14451684057712555,
76
+ "learning_rate": 0.00029110824742268037,
77
+ "loss": 0.9979846954345704,
78
+ "mean_token_accuracy": 0.7634965121746063,
79
+ "num_tokens": 183753.0,
80
+ "step": 70
81
+ },
82
+ {
83
+ "entropy": 0.9941512286663056,
84
+ "epoch": 0.06872852233676977,
85
+ "grad_norm": 0.15088772773742676,
86
+ "learning_rate": 0.00028981958762886595,
87
+ "loss": 0.9917948722839356,
88
+ "mean_token_accuracy": 0.7609466493129731,
89
+ "num_tokens": 209154.0,
90
+ "step": 80
91
+ },
92
+ {
93
+ "entropy": 0.899951022863388,
94
+ "epoch": 0.07731958762886598,
95
+ "grad_norm": 0.1635286509990692,
96
+ "learning_rate": 0.0002885309278350515,
97
+ "loss": 0.9084607124328613,
98
+ "mean_token_accuracy": 0.773688817024231,
99
+ "num_tokens": 232285.0,
100
+ "step": 90
101
+ },
102
+ {
103
+ "entropy": 0.9840596318244934,
104
+ "epoch": 0.0859106529209622,
105
+ "grad_norm": 0.1687513142824173,
106
+ "learning_rate": 0.0002872422680412371,
107
+ "loss": 1.005988883972168,
108
+ "mean_token_accuracy": 0.7565966248512268,
109
+ "num_tokens": 258165.0,
110
+ "step": 100
111
+ },
112
+ {
113
+ "entropy": 0.9256654202938079,
114
+ "epoch": 0.09450171821305842,
115
+ "grad_norm": 0.15977086126804352,
116
+ "learning_rate": 0.0002859536082474227,
117
+ "loss": 0.8968193054199218,
118
+ "mean_token_accuracy": 0.7728870630264282,
119
+ "num_tokens": 283613.0,
120
+ "step": 110
121
+ },
122
+ {
123
+ "entropy": 0.9430408120155335,
124
+ "epoch": 0.10309278350515463,
125
+ "grad_norm": 0.14840301871299744,
126
+ "learning_rate": 0.0002846649484536082,
127
+ "loss": 0.9234983444213867,
128
+ "mean_token_accuracy": 0.7652122378349304,
129
+ "num_tokens": 308257.0,
130
+ "step": 120
131
+ },
132
+ {
133
+ "entropy": 0.9537142395973206,
134
+ "epoch": 0.11168384879725086,
135
+ "grad_norm": 0.1754530817270279,
136
+ "learning_rate": 0.0002833762886597938,
137
+ "loss": 0.949033260345459,
138
+ "mean_token_accuracy": 0.7616437077522278,
139
+ "num_tokens": 333700.0,
140
+ "step": 130
141
+ },
142
+ {
143
+ "entropy": 0.9385378897190094,
144
+ "epoch": 0.12027491408934708,
145
+ "grad_norm": 0.16817958652973175,
146
+ "learning_rate": 0.00028208762886597935,
147
+ "loss": 0.9222366333007812,
148
+ "mean_token_accuracy": 0.7721138775348664,
149
+ "num_tokens": 359746.0,
150
+ "step": 140
151
+ },
152
+ {
153
+ "entropy": 0.8476657152175904,
154
+ "epoch": 0.12886597938144329,
155
+ "grad_norm": 0.15769453346729279,
156
+ "learning_rate": 0.0002807989690721649,
157
+ "loss": 0.8225071907043457,
158
+ "mean_token_accuracy": 0.7880932271480561,
159
+ "num_tokens": 383468.0,
160
+ "step": 150
161
+ },
162
+ {
163
+ "entropy": 0.8862757325172425,
164
+ "epoch": 0.13745704467353953,
165
+ "grad_norm": 0.17620904743671417,
166
+ "learning_rate": 0.0002795103092783505,
167
+ "loss": 0.8871653556823731,
168
+ "mean_token_accuracy": 0.7726393282413483,
169
+ "num_tokens": 409660.0,
170
+ "step": 160
171
+ },
172
+ {
173
+ "entropy": 0.9064472615718842,
174
+ "epoch": 0.14604810996563575,
175
+ "grad_norm": 0.15342549979686737,
176
+ "learning_rate": 0.0002782216494845361,
177
+ "loss": 0.9092922210693359,
178
+ "mean_token_accuracy": 0.7713103413581848,
179
+ "num_tokens": 437071.0,
180
+ "step": 170
181
+ },
182
+ {
183
+ "entropy": 0.9095455348491669,
184
+ "epoch": 0.15463917525773196,
185
+ "grad_norm": 0.17525391280651093,
186
+ "learning_rate": 0.00027693298969072165,
187
+ "loss": 0.8956180572509765,
188
+ "mean_token_accuracy": 0.7772489190101624,
189
+ "num_tokens": 460852.0,
190
+ "step": 180
191
+ },
192
+ {
193
+ "entropy": 0.8514094173908233,
194
+ "epoch": 0.16323024054982818,
195
+ "grad_norm": 0.15829823911190033,
196
+ "learning_rate": 0.0002756443298969072,
197
+ "loss": 0.8513413429260254,
198
+ "mean_token_accuracy": 0.779003256559372,
199
+ "num_tokens": 486114.0,
200
+ "step": 190
201
+ },
202
+ {
203
+ "entropy": 0.8995061337947845,
204
+ "epoch": 0.1718213058419244,
205
+ "grad_norm": 0.15622206032276154,
206
+ "learning_rate": 0.00027435567010309275,
207
+ "loss": 0.898340892791748,
208
+ "mean_token_accuracy": 0.7708433449268342,
209
+ "num_tokens": 512397.0,
210
+ "step": 200
211
+ },
212
+ {
213
+ "entropy": 0.8732269763946533,
214
+ "epoch": 0.18041237113402062,
215
+ "grad_norm": 0.17759552597999573,
216
+ "learning_rate": 0.0002730670103092783,
217
+ "loss": 0.8695596694946289,
218
+ "mean_token_accuracy": 0.7793173313140869,
219
+ "num_tokens": 536554.0,
220
+ "step": 210
221
+ },
222
+ {
223
+ "entropy": 0.9366106212139129,
224
+ "epoch": 0.18900343642611683,
225
+ "grad_norm": 0.1767597794532776,
226
+ "learning_rate": 0.0002717783505154639,
227
+ "loss": 0.9249121665954589,
228
+ "mean_token_accuracy": 0.7724991559982299,
229
+ "num_tokens": 561637.0,
230
+ "step": 220
231
+ },
232
+ {
233
+ "entropy": 0.8857321858406066,
234
+ "epoch": 0.19759450171821305,
235
+ "grad_norm": 0.20788735151290894,
236
+ "learning_rate": 0.0002704896907216495,
237
+ "loss": 0.8790401458740235,
238
+ "mean_token_accuracy": 0.7778670430183411,
239
+ "num_tokens": 586614.0,
240
+ "step": 230
241
+ },
242
+ {
243
+ "entropy": 0.9273143649101258,
244
+ "epoch": 0.20618556701030927,
245
+ "grad_norm": 0.16100187599658966,
246
+ "learning_rate": 0.00026920103092783505,
247
+ "loss": 0.9232232093811035,
248
+ "mean_token_accuracy": 0.7677364766597747,
249
+ "num_tokens": 612428.0,
250
+ "step": 240
251
+ },
252
+ {
253
+ "entropy": 0.8786617696285248,
254
+ "epoch": 0.21477663230240548,
255
+ "grad_norm": 0.1608886867761612,
256
+ "learning_rate": 0.00026791237113402063,
257
+ "loss": 0.8883329391479492,
258
+ "mean_token_accuracy": 0.7775621056556702,
259
+ "num_tokens": 638564.0,
260
+ "step": 250
261
+ },
262
+ {
263
+ "entropy": 0.9227964103221893,
264
+ "epoch": 0.22336769759450173,
265
+ "grad_norm": 0.1510680615901947,
266
+ "learning_rate": 0.00026662371134020615,
267
+ "loss": 0.9296038627624512,
268
+ "mean_token_accuracy": 0.7714014649391174,
269
+ "num_tokens": 665263.0,
270
+ "step": 260
271
+ },
272
+ {
273
+ "entropy": 0.9126189887523651,
274
+ "epoch": 0.23195876288659795,
275
+ "grad_norm": 0.16434459388256073,
276
+ "learning_rate": 0.00026533505154639173,
277
+ "loss": 0.9191689491271973,
278
+ "mean_token_accuracy": 0.7705935597419739,
279
+ "num_tokens": 691956.0,
280
+ "step": 270
281
+ },
282
+ {
283
+ "entropy": 0.8797955513000488,
284
+ "epoch": 0.24054982817869416,
285
+ "grad_norm": 0.1714707911014557,
286
+ "learning_rate": 0.0002640463917525773,
287
+ "loss": 0.8856119155883789,
288
+ "mean_token_accuracy": 0.7766761302947998,
289
+ "num_tokens": 716820.0,
290
+ "step": 280
291
+ },
292
+ {
293
+ "entropy": 0.877093505859375,
294
+ "epoch": 0.24914089347079038,
295
+ "grad_norm": 0.15326625108718872,
296
+ "learning_rate": 0.0002627577319587629,
297
+ "loss": 0.8566678047180176,
298
+ "mean_token_accuracy": 0.7812119901180268,
299
+ "num_tokens": 742221.0,
300
+ "step": 290
301
+ },
302
+ {
303
+ "entropy": 0.87042076587677,
304
+ "epoch": 0.25773195876288657,
305
+ "grad_norm": 0.1710847020149231,
306
+ "learning_rate": 0.00026146907216494846,
307
+ "loss": 0.8829298973083496,
308
+ "mean_token_accuracy": 0.7770332932472229,
309
+ "num_tokens": 765839.0,
310
+ "step": 300
311
+ },
312
+ {
313
+ "entropy": 0.8417458057403564,
314
+ "epoch": 0.2663230240549828,
315
+ "grad_norm": 0.1496821790933609,
316
+ "learning_rate": 0.00026018041237113403,
317
+ "loss": 0.8433545112609864,
318
+ "mean_token_accuracy": 0.7857938230037689,
319
+ "num_tokens": 791131.0,
320
+ "step": 310
321
+ },
322
+ {
323
+ "entropy": 0.9158999443054199,
324
+ "epoch": 0.27491408934707906,
325
+ "grad_norm": 0.17929035425186157,
326
+ "learning_rate": 0.00025889175257731955,
327
+ "loss": 0.9123490333557129,
328
+ "mean_token_accuracy": 0.775556480884552,
329
+ "num_tokens": 816984.0,
330
+ "step": 320
331
+ },
332
+ {
333
+ "entropy": 0.8314016401767731,
334
+ "epoch": 0.28350515463917525,
335
+ "grad_norm": 0.16051091253757477,
336
+ "learning_rate": 0.00025760309278350513,
337
+ "loss": 0.8279628753662109,
338
+ "mean_token_accuracy": 0.7862712442874908,
339
+ "num_tokens": 842220.0,
340
+ "step": 330
341
+ },
342
+ {
343
+ "entropy": 0.8677021145820618,
344
+ "epoch": 0.2920962199312715,
345
+ "grad_norm": 0.19157563149929047,
346
+ "learning_rate": 0.0002563144329896907,
347
+ "loss": 0.8552864074707032,
348
+ "mean_token_accuracy": 0.7835110187530517,
349
+ "num_tokens": 867868.0,
350
+ "step": 340
351
+ },
352
+ {
353
+ "entropy": 0.8919401407241822,
354
+ "epoch": 0.3006872852233677,
355
+ "grad_norm": 0.14258626103401184,
356
+ "learning_rate": 0.0002550257731958763,
357
+ "loss": 0.9019416809082031,
358
+ "mean_token_accuracy": 0.7760707855224609,
359
+ "num_tokens": 894676.0,
360
+ "step": 350
361
+ },
362
+ {
363
+ "entropy": 0.8415017545223236,
364
+ "epoch": 0.30927835051546393,
365
+ "grad_norm": 0.14830808341503143,
366
+ "learning_rate": 0.00025373711340206186,
367
+ "loss": 0.8072587013244629,
368
+ "mean_token_accuracy": 0.7912742376327515,
369
+ "num_tokens": 921632.0,
370
+ "step": 360
371
+ },
372
+ {
373
+ "entropy": 0.8371863842010498,
374
+ "epoch": 0.3178694158075601,
375
+ "grad_norm": 0.15538977086544037,
376
+ "learning_rate": 0.0002524484536082474,
377
+ "loss": 0.8308550834655761,
378
+ "mean_token_accuracy": 0.7849089920520782,
379
+ "num_tokens": 945955.0,
380
+ "step": 370
381
+ },
382
+ {
383
+ "entropy": 0.8338176369667053,
384
+ "epoch": 0.32646048109965636,
385
+ "grad_norm": 0.1711844801902771,
386
+ "learning_rate": 0.00025115979381443295,
387
+ "loss": 0.8479556083679199,
388
+ "mean_token_accuracy": 0.7850408136844635,
389
+ "num_tokens": 971508.0,
390
+ "step": 380
391
+ },
392
+ {
393
+ "entropy": 0.8876297354698182,
394
+ "epoch": 0.33505154639175255,
395
+ "grad_norm": 0.14902909100055695,
396
+ "learning_rate": 0.00024987113402061853,
397
+ "loss": 0.8821809768676758,
398
+ "mean_token_accuracy": 0.7764640510082245,
399
+ "num_tokens": 995124.0,
400
+ "step": 390
401
+ },
402
+ {
403
+ "entropy": 0.8212514638900756,
404
+ "epoch": 0.3436426116838488,
405
+ "grad_norm": 0.17950233817100525,
406
+ "learning_rate": 0.0002485824742268041,
407
+ "loss": 0.8255527496337891,
408
+ "mean_token_accuracy": 0.7870513379573822,
409
+ "num_tokens": 1019735.0,
410
+ "step": 400
411
+ },
412
+ {
413
+ "entropy": 0.9294387817382812,
414
+ "epoch": 0.35223367697594504,
415
+ "grad_norm": 0.15237703919410706,
416
+ "learning_rate": 0.0002472938144329897,
417
+ "loss": 0.9235431671142578,
418
+ "mean_token_accuracy": 0.7703655660152435,
419
+ "num_tokens": 1046295.0,
420
+ "step": 410
421
+ },
422
+ {
423
+ "entropy": 0.8429355442523956,
424
+ "epoch": 0.36082474226804123,
425
+ "grad_norm": 0.1589658558368683,
426
+ "learning_rate": 0.0002460051546391752,
427
+ "loss": 0.8329957008361817,
428
+ "mean_token_accuracy": 0.7897172749042511,
429
+ "num_tokens": 1072020.0,
430
+ "step": 420
431
+ },
432
+ {
433
+ "entropy": 0.8407810032367706,
434
+ "epoch": 0.3694158075601375,
435
+ "grad_norm": 0.17521116137504578,
436
+ "learning_rate": 0.0002447164948453608,
437
+ "loss": 0.8535200119018554,
438
+ "mean_token_accuracy": 0.7809976935386658,
439
+ "num_tokens": 1098420.0,
440
+ "step": 430
441
+ },
442
+ {
443
+ "entropy": 0.8515082120895385,
444
+ "epoch": 0.37800687285223367,
445
+ "grad_norm": 0.15218117833137512,
446
+ "learning_rate": 0.00024342783505154638,
447
+ "loss": 0.8459153175354004,
448
+ "mean_token_accuracy": 0.7833445191383361,
449
+ "num_tokens": 1124235.0,
450
+ "step": 440
451
+ },
452
+ {
453
+ "entropy": 0.8506015419960022,
454
+ "epoch": 0.3865979381443299,
455
+ "grad_norm": 0.1809423863887787,
456
+ "learning_rate": 0.00024213917525773193,
457
+ "loss": 0.8384982109069824,
458
+ "mean_token_accuracy": 0.7841372966766358,
459
+ "num_tokens": 1149636.0,
460
+ "step": 450
461
+ },
462
+ {
463
+ "entropy": 0.9110018074512481,
464
+ "epoch": 0.3951890034364261,
465
+ "grad_norm": 0.15085963904857635,
466
+ "learning_rate": 0.0002408505154639175,
467
+ "loss": 0.9191832542419434,
468
+ "mean_token_accuracy": 0.7757422685623169,
469
+ "num_tokens": 1173711.0,
470
+ "step": 460
471
+ },
472
+ {
473
+ "entropy": 0.9000201225280762,
474
+ "epoch": 0.40378006872852235,
475
+ "grad_norm": 0.16748838126659393,
476
+ "learning_rate": 0.00023956185567010308,
477
+ "loss": 0.9107230186462403,
478
+ "mean_token_accuracy": 0.7768173336982727,
479
+ "num_tokens": 1199340.0,
480
+ "step": 470
481
+ },
482
+ {
483
+ "entropy": 0.8751251935958863,
484
+ "epoch": 0.41237113402061853,
485
+ "grad_norm": 0.1593722254037857,
486
+ "learning_rate": 0.00023827319587628863,
487
+ "loss": 0.8804635047912598,
488
+ "mean_token_accuracy": 0.7814023733139038,
489
+ "num_tokens": 1226032.0,
490
+ "step": 480
491
+ },
492
+ {
493
+ "entropy": 0.8843371510505676,
494
+ "epoch": 0.4209621993127148,
495
+ "grad_norm": 0.12278851121664047,
496
+ "learning_rate": 0.0002369845360824742,
497
+ "loss": 0.8643080711364746,
498
+ "mean_token_accuracy": 0.7809630572795868,
499
+ "num_tokens": 1252769.0,
500
+ "step": 490
501
+ },
502
+ {
503
+ "entropy": 0.8834336459636688,
504
+ "epoch": 0.42955326460481097,
505
+ "grad_norm": 0.1367531716823578,
506
+ "learning_rate": 0.00023569587628865976,
507
+ "loss": 0.8869522094726563,
508
+ "mean_token_accuracy": 0.7775909185409546,
509
+ "num_tokens": 1278979.0,
510
+ "step": 500
511
+ },
512
+ {
513
+ "entropy": 0.9062523484230042,
514
+ "epoch": 0.4381443298969072,
515
+ "grad_norm": 0.14829133450984955,
516
+ "learning_rate": 0.00023440721649484533,
517
+ "loss": 0.9153310775756835,
518
+ "mean_token_accuracy": 0.7755910754203796,
519
+ "num_tokens": 1305141.0,
520
+ "step": 510
521
+ },
522
+ {
523
+ "entropy": 0.8907318472862243,
524
+ "epoch": 0.44673539518900346,
525
+ "grad_norm": 0.19640029966831207,
526
+ "learning_rate": 0.0002331185567010309,
527
+ "loss": 0.8736312866210938,
528
+ "mean_token_accuracy": 0.7823721885681152,
529
+ "num_tokens": 1333535.0,
530
+ "step": 520
531
+ },
532
+ {
533
+ "entropy": 0.9001505076885223,
534
+ "epoch": 0.45532646048109965,
535
+ "grad_norm": 0.1615416407585144,
536
+ "learning_rate": 0.00023182989690721648,
537
+ "loss": 0.9240070343017578,
538
+ "mean_token_accuracy": 0.7720246851444245,
539
+ "num_tokens": 1359100.0,
540
+ "step": 530
541
+ },
542
+ {
543
+ "entropy": 0.8964834988117218,
544
+ "epoch": 0.4639175257731959,
545
+ "grad_norm": 0.16148629784584045,
546
+ "learning_rate": 0.00023054123711340206,
547
+ "loss": 0.9040334701538086,
548
+ "mean_token_accuracy": 0.7697900414466858,
549
+ "num_tokens": 1386642.0,
550
+ "step": 540
551
+ },
552
+ {
553
+ "entropy": 0.9152944028377533,
554
+ "epoch": 0.4725085910652921,
555
+ "grad_norm": 0.16060467064380646,
556
+ "learning_rate": 0.00022925257731958758,
557
+ "loss": 0.9094497680664062,
558
+ "mean_token_accuracy": 0.771862530708313,
559
+ "num_tokens": 1412712.0,
560
+ "step": 550
561
+ },
562
+ {
563
+ "entropy": 0.8443439364433288,
564
+ "epoch": 0.48109965635738833,
565
+ "grad_norm": 0.16139253973960876,
566
+ "learning_rate": 0.00022796391752577316,
567
+ "loss": 0.8445466041564942,
568
+ "mean_token_accuracy": 0.7862408220767975,
569
+ "num_tokens": 1439208.0,
570
+ "step": 560
571
+ },
572
+ {
573
+ "entropy": 0.8669894218444825,
574
+ "epoch": 0.4896907216494845,
575
+ "grad_norm": 0.13149064779281616,
576
+ "learning_rate": 0.00022667525773195873,
577
+ "loss": 0.8791823387145996,
578
+ "mean_token_accuracy": 0.7771342694759369,
579
+ "num_tokens": 1466602.0,
580
+ "step": 570
581
+ },
582
+ {
583
+ "entropy": 0.8713687837123871,
584
+ "epoch": 0.49828178694158076,
585
+ "grad_norm": 0.15979787707328796,
586
+ "learning_rate": 0.0002253865979381443,
587
+ "loss": 0.8594341278076172,
588
+ "mean_token_accuracy": 0.7810667157173157,
589
+ "num_tokens": 1493048.0,
590
+ "step": 580
591
+ },
592
+ {
593
+ "entropy": 0.9155345737934113,
594
+ "epoch": 0.506872852233677,
595
+ "grad_norm": 0.18420852720737457,
596
+ "learning_rate": 0.00022409793814432989,
597
+ "loss": 0.915378475189209,
598
+ "mean_token_accuracy": 0.7724271833896637,
599
+ "num_tokens": 1517512.0,
600
+ "step": 590
601
+ },
602
+ {
603
+ "entropy": 0.891716331243515,
604
+ "epoch": 0.5154639175257731,
605
+ "grad_norm": 0.1665642410516739,
606
+ "learning_rate": 0.00022280927835051546,
607
+ "loss": 0.8950610160827637,
608
+ "mean_token_accuracy": 0.777475905418396,
609
+ "num_tokens": 1542765.0,
610
+ "step": 600
611
+ },
612
+ {
613
+ "entropy": 0.9547060489654541,
614
+ "epoch": 0.5240549828178694,
615
+ "grad_norm": 0.19496814906597137,
616
+ "learning_rate": 0.000221520618556701,
617
+ "loss": 0.9572202682495117,
618
+ "mean_token_accuracy": 0.7599396347999573,
619
+ "num_tokens": 1570704.0,
620
+ "step": 610
621
+ },
622
+ {
623
+ "entropy": 0.894726449251175,
624
+ "epoch": 0.5326460481099656,
625
+ "grad_norm": 0.162724569439888,
626
+ "learning_rate": 0.00022023195876288656,
627
+ "loss": 0.8876501083374023,
628
+ "mean_token_accuracy": 0.7815112411975861,
629
+ "num_tokens": 1594675.0,
630
+ "step": 620
631
+ },
632
+ {
633
+ "entropy": 0.8966928541660308,
634
+ "epoch": 0.5412371134020618,
635
+ "grad_norm": 0.1820981204509735,
636
+ "learning_rate": 0.00021894329896907214,
637
+ "loss": 0.8992101669311523,
638
+ "mean_token_accuracy": 0.7774552226066589,
639
+ "num_tokens": 1621381.0,
640
+ "step": 630
641
+ },
642
+ {
643
+ "entropy": 0.8320938229560852,
644
+ "epoch": 0.5498281786941581,
645
+ "grad_norm": 0.18641315400600433,
646
+ "learning_rate": 0.0002176546391752577,
647
+ "loss": 0.8333114624023438,
648
+ "mean_token_accuracy": 0.7836415946483613,
649
+ "num_tokens": 1646589.0,
650
+ "step": 640
651
+ },
652
+ {
653
+ "entropy": 0.9350853085517883,
654
+ "epoch": 0.5584192439862543,
655
+ "grad_norm": 0.15154911577701569,
656
+ "learning_rate": 0.0002163659793814433,
657
+ "loss": 0.9300890922546386,
658
+ "mean_token_accuracy": 0.7711542308330536,
659
+ "num_tokens": 1672281.0,
660
+ "step": 650
661
+ },
662
+ {
663
+ "entropy": 0.8372068881988526,
664
+ "epoch": 0.5670103092783505,
665
+ "grad_norm": 0.157988041639328,
666
+ "learning_rate": 0.00021507731958762884,
667
+ "loss": 0.8490092277526855,
668
+ "mean_token_accuracy": 0.7828391373157502,
669
+ "num_tokens": 1697203.0,
670
+ "step": 660
671
+ },
672
+ {
673
+ "entropy": 0.8175156831741333,
674
+ "epoch": 0.5756013745704467,
675
+ "grad_norm": 0.16615159809589386,
676
+ "learning_rate": 0.0002137886597938144,
677
+ "loss": 0.7962069988250733,
678
+ "mean_token_accuracy": 0.793628740310669,
679
+ "num_tokens": 1722977.0,
680
+ "step": 670
681
+ },
682
+ {
683
+ "entropy": 0.9293979525566101,
684
+ "epoch": 0.584192439862543,
685
+ "grad_norm": 0.13969369232654572,
686
+ "learning_rate": 0.0002125,
687
+ "loss": 0.9364766120910645,
688
+ "mean_token_accuracy": 0.7693193078041076,
689
+ "num_tokens": 1748869.0,
690
+ "step": 680
691
+ },
692
+ {
693
+ "entropy": 0.8958774268627167,
694
+ "epoch": 0.5927835051546392,
695
+ "grad_norm": 0.14425267279148102,
696
+ "learning_rate": 0.00021121134020618556,
697
+ "loss": 0.8842126846313476,
698
+ "mean_token_accuracy": 0.7759437382221221,
699
+ "num_tokens": 1774715.0,
700
+ "step": 690
701
+ },
702
+ {
703
+ "entropy": 0.929528433084488,
704
+ "epoch": 0.6013745704467354,
705
+ "grad_norm": 0.15875831246376038,
706
+ "learning_rate": 0.0002099226804123711,
707
+ "loss": 0.9353519439697265,
708
+ "mean_token_accuracy": 0.769885140657425,
709
+ "num_tokens": 1800430.0,
710
+ "step": 700
711
+ },
712
+ {
713
+ "entropy": 0.8651838064193725,
714
+ "epoch": 0.6099656357388317,
715
+ "grad_norm": 0.19464287161827087,
716
+ "learning_rate": 0.00020863402061855666,
717
+ "loss": 0.8678315162658692,
718
+ "mean_token_accuracy": 0.7783544957637787,
719
+ "num_tokens": 1824631.0,
720
+ "step": 710
721
+ },
722
+ {
723
+ "entropy": 0.874721884727478,
724
+ "epoch": 0.6185567010309279,
725
+ "grad_norm": 0.17015859484672546,
726
+ "learning_rate": 0.00020734536082474224,
727
+ "loss": 0.847961139678955,
728
+ "mean_token_accuracy": 0.7813271641731262,
729
+ "num_tokens": 1849593.0,
730
+ "step": 720
731
+ },
732
+ {
733
+ "entropy": 0.8706691145896912,
734
+ "epoch": 0.627147766323024,
735
+ "grad_norm": 0.15960460901260376,
736
+ "learning_rate": 0.00020605670103092781,
737
+ "loss": 0.8743976593017578,
738
+ "mean_token_accuracy": 0.7772314369678497,
739
+ "num_tokens": 1874593.0,
740
+ "step": 730
741
+ },
742
+ {
743
+ "entropy": 0.824019831418991,
744
+ "epoch": 0.6357388316151202,
745
+ "grad_norm": 0.16192808747291565,
746
+ "learning_rate": 0.0002047680412371134,
747
+ "loss": 0.8214377403259278,
748
+ "mean_token_accuracy": 0.7877136409282685,
749
+ "num_tokens": 1899592.0,
750
+ "step": 740
751
+ },
752
+ {
753
+ "entropy": 0.853943508863449,
754
+ "epoch": 0.6443298969072165,
755
+ "grad_norm": 0.14331476390361786,
756
+ "learning_rate": 0.00020347938144329897,
757
+ "loss": 0.8542372703552246,
758
+ "mean_token_accuracy": 0.7825242638587951,
759
+ "num_tokens": 1925105.0,
760
+ "step": 750
761
+ },
762
+ {
763
+ "entropy": 0.9206405401229858,
764
+ "epoch": 0.6529209621993127,
765
+ "grad_norm": 0.16269327700138092,
766
+ "learning_rate": 0.00020219072164948454,
767
+ "loss": 0.9131902694702149,
768
+ "mean_token_accuracy": 0.7722071945667267,
769
+ "num_tokens": 1952126.0,
770
+ "step": 760
771
+ },
772
+ {
773
+ "entropy": 0.8438917219638824,
774
+ "epoch": 0.6615120274914089,
775
+ "grad_norm": 0.18783624470233917,
776
+ "learning_rate": 0.00020090206185567006,
777
+ "loss": 0.8612903594970703,
778
+ "mean_token_accuracy": 0.7805359125137329,
779
+ "num_tokens": 1976727.0,
780
+ "step": 770
781
+ },
782
+ {
783
+ "entropy": 0.8782703518867493,
784
+ "epoch": 0.6701030927835051,
785
+ "grad_norm": 0.1749051958322525,
786
+ "learning_rate": 0.00019961340206185564,
787
+ "loss": 0.8877654075622559,
788
+ "mean_token_accuracy": 0.775201940536499,
789
+ "num_tokens": 2004204.0,
790
+ "step": 780
791
+ },
792
+ {
793
+ "entropy": 0.9036802053451538,
794
+ "epoch": 0.6786941580756014,
795
+ "grad_norm": 0.17728140950202942,
796
+ "learning_rate": 0.00019832474226804121,
797
+ "loss": 0.8946940422058105,
798
+ "mean_token_accuracy": 0.7781059086322785,
799
+ "num_tokens": 2031926.0,
800
+ "step": 790
801
+ },
802
+ {
803
+ "entropy": 0.9057375133037567,
804
+ "epoch": 0.6872852233676976,
805
+ "grad_norm": 0.1804327517747879,
806
+ "learning_rate": 0.0001970360824742268,
807
+ "loss": 0.9067662239074707,
808
+ "mean_token_accuracy": 0.7786577641963959,
809
+ "num_tokens": 2059628.0,
810
+ "step": 800
811
+ },
812
+ {
813
+ "entropy": 0.8421910405158997,
814
+ "epoch": 0.6958762886597938,
815
+ "grad_norm": 0.1570490598678589,
816
+ "learning_rate": 0.00019574742268041237,
817
+ "loss": 0.8346380233764649,
818
+ "mean_token_accuracy": 0.7839872419834137,
819
+ "num_tokens": 2083943.0,
820
+ "step": 810
821
+ },
822
+ {
823
+ "entropy": 0.8743102788925171,
824
+ "epoch": 0.7044673539518901,
825
+ "grad_norm": 0.1737687736749649,
826
+ "learning_rate": 0.00019445876288659794,
827
+ "loss": 0.8622690200805664,
828
+ "mean_token_accuracy": 0.7791435241699218,
829
+ "num_tokens": 2109444.0,
830
+ "step": 820
831
+ },
832
+ {
833
+ "entropy": 0.8926449298858643,
834
+ "epoch": 0.7130584192439863,
835
+ "grad_norm": 0.1972922682762146,
836
+ "learning_rate": 0.0001931701030927835,
837
+ "loss": 0.895075511932373,
838
+ "mean_token_accuracy": 0.7716107368469238,
839
+ "num_tokens": 2134209.0,
840
+ "step": 830
841
+ },
842
+ {
843
+ "entropy": 0.8712981760501861,
844
+ "epoch": 0.7216494845360825,
845
+ "grad_norm": 0.16580969095230103,
846
+ "learning_rate": 0.00019188144329896904,
847
+ "loss": 0.8615394592285156,
848
+ "mean_token_accuracy": 0.780440878868103,
849
+ "num_tokens": 2160322.0,
850
+ "step": 840
851
+ },
852
+ {
853
+ "entropy": 0.7872600853443146,
854
+ "epoch": 0.7302405498281787,
855
+ "grad_norm": 0.17089906334877014,
856
+ "learning_rate": 0.00019059278350515462,
857
+ "loss": 0.7738356590270996,
858
+ "mean_token_accuracy": 0.7984008610248565,
859
+ "num_tokens": 2186673.0,
860
+ "step": 850
861
+ },
862
+ {
863
+ "entropy": 0.8024394273757934,
864
+ "epoch": 0.738831615120275,
865
+ "grad_norm": 0.15015770494937897,
866
+ "learning_rate": 0.0001893041237113402,
867
+ "loss": 0.801088809967041,
868
+ "mean_token_accuracy": 0.7919513285160065,
869
+ "num_tokens": 2211288.0,
870
+ "step": 860
871
+ },
872
+ {
873
+ "entropy": 0.8289069414138794,
874
+ "epoch": 0.7474226804123711,
875
+ "grad_norm": 0.15804092586040497,
876
+ "learning_rate": 0.00018801546391752577,
877
+ "loss": 0.8228999137878418,
878
+ "mean_token_accuracy": 0.7900383174419403,
879
+ "num_tokens": 2235494.0,
880
+ "step": 870
881
+ },
882
+ {
883
+ "entropy": 0.8337613046169281,
884
+ "epoch": 0.7560137457044673,
885
+ "grad_norm": 0.16564202308654785,
886
+ "learning_rate": 0.00018672680412371132,
887
+ "loss": 0.8357515335083008,
888
+ "mean_token_accuracy": 0.7796721041202546,
889
+ "num_tokens": 2261853.0,
890
+ "step": 880
891
+ },
892
+ {
893
+ "entropy": 0.8514188408851624,
894
+ "epoch": 0.7646048109965635,
895
+ "grad_norm": 0.15763898193836212,
896
+ "learning_rate": 0.0001854381443298969,
897
+ "loss": 0.8596377372741699,
898
+ "mean_token_accuracy": 0.781908392906189,
899
+ "num_tokens": 2288235.0,
900
+ "step": 890
901
+ },
902
+ {
903
+ "entropy": 0.8182059168815613,
904
+ "epoch": 0.7731958762886598,
905
+ "grad_norm": 0.14843082427978516,
906
+ "learning_rate": 0.00018414948453608247,
907
+ "loss": 0.8056727409362793,
908
+ "mean_token_accuracy": 0.7887085616588593,
909
+ "num_tokens": 2312636.0,
910
+ "step": 900
911
+ },
912
+ {
913
+ "entropy": 0.8019815027713776,
914
+ "epoch": 0.781786941580756,
915
+ "grad_norm": 0.1444498896598816,
916
+ "learning_rate": 0.00018286082474226804,
917
+ "loss": 0.7791801452636719,
918
+ "mean_token_accuracy": 0.7921311616897583,
919
+ "num_tokens": 2337590.0,
920
+ "step": 910
921
+ },
922
+ {
923
+ "entropy": 0.8811851978302002,
924
+ "epoch": 0.7903780068728522,
925
+ "grad_norm": 0.20295798778533936,
926
+ "learning_rate": 0.0001815721649484536,
927
+ "loss": 0.9093887329101562,
928
+ "mean_token_accuracy": 0.7765615165233613,
929
+ "num_tokens": 2364733.0,
930
+ "step": 920
931
+ },
932
+ {
933
+ "entropy": 0.8845545649528503,
934
+ "epoch": 0.7989690721649485,
935
+ "grad_norm": 0.16211840510368347,
936
+ "learning_rate": 0.00018028350515463914,
937
+ "loss": 0.87130765914917,
938
+ "mean_token_accuracy": 0.773591297864914,
939
+ "num_tokens": 2389138.0,
940
+ "step": 930
941
+ },
942
+ {
943
+ "entropy": 0.8366316437721253,
944
+ "epoch": 0.8075601374570447,
945
+ "grad_norm": 0.1796175092458725,
946
+ "learning_rate": 0.00017899484536082472,
947
+ "loss": 0.8393503189086914,
948
+ "mean_token_accuracy": 0.7834821224212647,
949
+ "num_tokens": 2416288.0,
950
+ "step": 940
951
+ },
952
+ {
953
+ "entropy": 0.8687749207019806,
954
+ "epoch": 0.8161512027491409,
955
+ "grad_norm": 0.17602433264255524,
956
+ "learning_rate": 0.0001777061855670103,
957
+ "loss": 0.8831927299499511,
958
+ "mean_token_accuracy": 0.7806714773178101,
959
+ "num_tokens": 2442282.0,
960
+ "step": 950
961
+ },
962
+ {
963
+ "entropy": 0.8602447748184204,
964
+ "epoch": 0.8247422680412371,
965
+ "grad_norm": 0.14779216051101685,
966
+ "learning_rate": 0.00017641752577319587,
967
+ "loss": 0.8557798385620117,
968
+ "mean_token_accuracy": 0.7800233542919159,
969
+ "num_tokens": 2469865.0,
970
+ "step": 960
971
+ },
972
+ {
973
+ "entropy": 0.8987692832946778,
974
+ "epoch": 0.8333333333333334,
975
+ "grad_norm": 0.16065306961536407,
976
+ "learning_rate": 0.00017512886597938145,
977
+ "loss": 0.8930519104003907,
978
+ "mean_token_accuracy": 0.7771966874599456,
979
+ "num_tokens": 2497716.0,
980
+ "step": 970
981
+ },
982
+ {
983
+ "entropy": 0.847240948677063,
984
+ "epoch": 0.8419243986254296,
985
+ "grad_norm": 0.16063280403614044,
986
+ "learning_rate": 0.00017384020618556702,
987
+ "loss": 0.8212727546691895,
988
+ "mean_token_accuracy": 0.7900629937648773,
989
+ "num_tokens": 2523292.0,
990
+ "step": 980
991
+ },
992
+ {
993
+ "entropy": 0.8685175359249115,
994
+ "epoch": 0.8505154639175257,
995
+ "grad_norm": 0.19293011724948883,
996
+ "learning_rate": 0.00017255154639175254,
997
+ "loss": 0.8839738845825196,
998
+ "mean_token_accuracy": 0.7780547857284545,
999
+ "num_tokens": 2547529.0,
1000
+ "step": 990
1001
+ },
1002
+ {
1003
+ "entropy": 0.8589172720909118,
1004
+ "epoch": 0.8591065292096219,
1005
+ "grad_norm": 0.13081640005111694,
1006
+ "learning_rate": 0.00017126288659793812,
1007
+ "loss": 0.8760468482971191,
1008
+ "mean_token_accuracy": 0.7810062050819397,
1009
+ "num_tokens": 2574943.0,
1010
+ "step": 1000
1011
+ },
1012
+ {
1013
+ "entropy": 0.8676146745681763,
1014
+ "epoch": 0.8676975945017182,
1015
+ "grad_norm": 0.18423762917518616,
1016
+ "learning_rate": 0.0001699742268041237,
1017
+ "loss": 0.8576597213745117,
1018
+ "mean_token_accuracy": 0.7845786452293396,
1019
+ "num_tokens": 2599770.0,
1020
+ "step": 1010
1021
+ },
1022
+ {
1023
+ "entropy": 0.8578267991542816,
1024
+ "epoch": 0.8762886597938144,
1025
+ "grad_norm": 0.15971733629703522,
1026
+ "learning_rate": 0.00016868556701030927,
1027
+ "loss": 0.8597639083862305,
1028
+ "mean_token_accuracy": 0.7836935818195343,
1029
+ "num_tokens": 2629050.0,
1030
+ "step": 1020
1031
+ },
1032
+ {
1033
+ "entropy": 0.78883256316185,
1034
+ "epoch": 0.8848797250859106,
1035
+ "grad_norm": 0.19334255158901215,
1036
+ "learning_rate": 0.00016739690721649485,
1037
+ "loss": 0.775438642501831,
1038
+ "mean_token_accuracy": 0.7994126856327057,
1039
+ "num_tokens": 2652418.0,
1040
+ "step": 1030
1041
+ },
1042
+ {
1043
+ "entropy": 0.827907818555832,
1044
+ "epoch": 0.8934707903780069,
1045
+ "grad_norm": 0.17899778485298157,
1046
+ "learning_rate": 0.0001661082474226804,
1047
+ "loss": 0.8155632019042969,
1048
+ "mean_token_accuracy": 0.7867587625980377,
1049
+ "num_tokens": 2677020.0,
1050
+ "step": 1040
1051
+ },
1052
+ {
1053
+ "entropy": 0.8488841831684113,
1054
+ "epoch": 0.9020618556701031,
1055
+ "grad_norm": 0.17594760656356812,
1056
+ "learning_rate": 0.00016481958762886597,
1057
+ "loss": 0.8443450927734375,
1058
+ "mean_token_accuracy": 0.7823632657527924,
1059
+ "num_tokens": 2700589.0,
1060
+ "step": 1050
1061
+ },
1062
+ {
1063
+ "entropy": 0.9077254295349121,
1064
+ "epoch": 0.9106529209621993,
1065
+ "grad_norm": 0.17107197642326355,
1066
+ "learning_rate": 0.00016353092783505152,
1067
+ "loss": 0.9043283462524414,
1068
+ "mean_token_accuracy": 0.7685754477977753,
1069
+ "num_tokens": 2727925.0,
1070
+ "step": 1060
1071
+ },
1072
+ {
1073
+ "entropy": 0.8681511282920837,
1074
+ "epoch": 0.9192439862542955,
1075
+ "grad_norm": 0.16261200606822968,
1076
+ "learning_rate": 0.0001622422680412371,
1077
+ "loss": 0.8636059761047363,
1078
+ "mean_token_accuracy": 0.7801066517829895,
1079
+ "num_tokens": 2754434.0,
1080
+ "step": 1070
1081
+ },
1082
+ {
1083
+ "entropy": 0.8201885402202607,
1084
+ "epoch": 0.9278350515463918,
1085
+ "grad_norm": 0.15494069457054138,
1086
+ "learning_rate": 0.00016095360824742267,
1087
+ "loss": 0.8267613410949707,
1088
+ "mean_token_accuracy": 0.7909399390220642,
1089
+ "num_tokens": 2778856.0,
1090
+ "step": 1080
1091
+ },
1092
+ {
1093
+ "entropy": 0.8281429171562195,
1094
+ "epoch": 0.936426116838488,
1095
+ "grad_norm": 0.15491366386413574,
1096
+ "learning_rate": 0.00015966494845360822,
1097
+ "loss": 0.8283931732177734,
1098
+ "mean_token_accuracy": 0.7858192563056946,
1099
+ "num_tokens": 2802467.0,
1100
+ "step": 1090
1101
+ },
1102
+ {
1103
+ "entropy": 0.8585192024707794,
1104
+ "epoch": 0.9450171821305842,
1105
+ "grad_norm": 0.16744765639305115,
1106
+ "learning_rate": 0.0001583762886597938,
1107
+ "loss": 0.8450031280517578,
1108
+ "mean_token_accuracy": 0.7824375092983246,
1109
+ "num_tokens": 2828404.0,
1110
+ "step": 1100
1111
+ },
1112
+ {
1113
+ "entropy": 0.9703075528144837,
1114
+ "epoch": 0.9536082474226805,
1115
+ "grad_norm": 0.16334781050682068,
1116
+ "learning_rate": 0.00015708762886597937,
1117
+ "loss": 0.9652547836303711,
1118
+ "mean_token_accuracy": 0.7639409124851226,
1119
+ "num_tokens": 2854621.0,
1120
+ "step": 1110
1121
+ },
1122
+ {
1123
+ "entropy": 0.8723820388317108,
1124
+ "epoch": 0.9621993127147767,
1125
+ "grad_norm": 0.15771399438381195,
1126
+ "learning_rate": 0.00015579896907216495,
1127
+ "loss": 0.8663213729858399,
1128
+ "mean_token_accuracy": 0.7808757722377777,
1129
+ "num_tokens": 2880448.0,
1130
+ "step": 1120
1131
+ },
1132
+ {
1133
+ "entropy": 0.8375514090061188,
1134
+ "epoch": 0.9707903780068728,
1135
+ "grad_norm": 0.16101613640785217,
1136
+ "learning_rate": 0.0001545103092783505,
1137
+ "loss": 0.8438360214233398,
1138
+ "mean_token_accuracy": 0.7864978194236756,
1139
+ "num_tokens": 2907346.0,
1140
+ "step": 1130
1141
+ },
1142
+ {
1143
+ "entropy": 0.836071914434433,
1144
+ "epoch": 0.979381443298969,
1145
+ "grad_norm": 0.19959381222724915,
1146
+ "learning_rate": 0.00015322164948453605,
1147
+ "loss": 0.8347750663757324,
1148
+ "mean_token_accuracy": 0.7878862261772156,
1149
+ "num_tokens": 2932769.0,
1150
+ "step": 1140
1151
+ },
1152
+ {
1153
+ "entropy": 0.8218921303749085,
1154
+ "epoch": 0.9879725085910653,
1155
+ "grad_norm": 0.17999783158302307,
1156
+ "learning_rate": 0.00015193298969072162,
1157
+ "loss": 0.81912841796875,
1158
+ "mean_token_accuracy": 0.7904701411724091,
1159
+ "num_tokens": 2956439.0,
1160
+ "step": 1150
1161
+ },
1162
+ {
1163
+ "entropy": 0.8836544632911683,
1164
+ "epoch": 0.9965635738831615,
1165
+ "grad_norm": 0.1764528602361679,
1166
+ "learning_rate": 0.0001506443298969072,
1167
+ "loss": 0.8860954284667969,
1168
+ "mean_token_accuracy": 0.7801528871059418,
1169
+ "num_tokens": 2983309.0,
1170
+ "step": 1160
1171
+ },
1172
+ {
1173
+ "entropy": 0.8893479645252228,
1174
+ "epoch": 1.0051546391752577,
1175
+ "grad_norm": 0.153923898935318,
1176
+ "learning_rate": 0.00014935567010309277,
1177
+ "loss": 0.8747451782226563,
1178
+ "mean_token_accuracy": 0.7765235006809235,
1179
+ "num_tokens": 3010175.0,
1180
+ "step": 1170
1181
+ },
1182
+ {
1183
+ "entropy": 0.8393925905227662,
1184
+ "epoch": 1.013745704467354,
1185
+ "grad_norm": 0.16801179945468903,
1186
+ "learning_rate": 0.00014806701030927835,
1187
+ "loss": 0.8316682815551758,
1188
+ "mean_token_accuracy": 0.7851088464260101,
1189
+ "num_tokens": 3036705.0,
1190
+ "step": 1180
1191
+ },
1192
+ {
1193
+ "entropy": 0.8521030902862549,
1194
+ "epoch": 1.02233676975945,
1195
+ "grad_norm": 0.16726894676685333,
1196
+ "learning_rate": 0.0001467783505154639,
1197
+ "loss": 0.8441553115844727,
1198
+ "mean_token_accuracy": 0.7883111536502838,
1199
+ "num_tokens": 3062184.0,
1200
+ "step": 1190
1201
+ },
1202
+ {
1203
+ "entropy": 0.8722131133079529,
1204
+ "epoch": 1.0309278350515463,
1205
+ "grad_norm": 0.1777733713388443,
1206
+ "learning_rate": 0.00014548969072164948,
1207
+ "loss": 0.8671235084533692,
1208
+ "mean_token_accuracy": 0.7776070535182953,
1209
+ "num_tokens": 3089854.0,
1210
+ "step": 1200
1211
+ },
1212
+ {
1213
+ "entropy": 0.8229423344135285,
1214
+ "epoch": 1.0395189003436427,
1215
+ "grad_norm": 0.13976716995239258,
1216
+ "learning_rate": 0.00014420103092783505,
1217
+ "loss": 0.8249235153198242,
1218
+ "mean_token_accuracy": 0.7889478981494904,
1219
+ "num_tokens": 3116938.0,
1220
+ "step": 1210
1221
+ },
1222
+ {
1223
+ "entropy": 0.8179661929607391,
1224
+ "epoch": 1.0481099656357389,
1225
+ "grad_norm": 0.16781210899353027,
1226
+ "learning_rate": 0.0001429123711340206,
1227
+ "loss": 0.8102549552917481,
1228
+ "mean_token_accuracy": 0.7927488446235657,
1229
+ "num_tokens": 3143825.0,
1230
+ "step": 1220
1231
+ },
1232
+ {
1233
+ "entropy": 0.7997653067111969,
1234
+ "epoch": 1.056701030927835,
1235
+ "grad_norm": 0.1647312045097351,
1236
+ "learning_rate": 0.00014162371134020618,
1237
+ "loss": 0.7900454998016357,
1238
+ "mean_token_accuracy": 0.7910003781318664,
1239
+ "num_tokens": 3168362.0,
1240
+ "step": 1230
1241
+ },
1242
+ {
1243
+ "entropy": 0.8539471685886383,
1244
+ "epoch": 1.0652920962199313,
1245
+ "grad_norm": 0.16146644949913025,
1246
+ "learning_rate": 0.00014033505154639175,
1247
+ "loss": 0.8489527702331543,
1248
+ "mean_token_accuracy": 0.7828418910503387,
1249
+ "num_tokens": 3192789.0,
1250
+ "step": 1240
1251
+ },
1252
+ {
1253
+ "entropy": 0.7987524449825287,
1254
+ "epoch": 1.0738831615120275,
1255
+ "grad_norm": 0.16997887194156647,
1256
+ "learning_rate": 0.0001390463917525773,
1257
+ "loss": 0.7815001964569092,
1258
+ "mean_token_accuracy": 0.7958592414855957,
1259
+ "num_tokens": 3218189.0,
1260
+ "step": 1250
1261
+ },
1262
+ {
1263
+ "entropy": 0.8433911919593811,
1264
+ "epoch": 1.0824742268041236,
1265
+ "grad_norm": 0.20293352007865906,
1266
+ "learning_rate": 0.00013775773195876288,
1267
+ "loss": 0.8536633491516114,
1268
+ "mean_token_accuracy": 0.7847901046276092,
1269
+ "num_tokens": 3242357.0,
1270
+ "step": 1260
1271
+ },
1272
+ {
1273
+ "entropy": 0.7989895343780518,
1274
+ "epoch": 1.0910652920962198,
1275
+ "grad_norm": 0.17577318847179413,
1276
+ "learning_rate": 0.00013646907216494845,
1277
+ "loss": 0.795603084564209,
1278
+ "mean_token_accuracy": 0.7937948822975158,
1279
+ "num_tokens": 3266634.0,
1280
+ "step": 1270
1281
+ },
1282
+ {
1283
+ "entropy": 0.849328339099884,
1284
+ "epoch": 1.0996563573883162,
1285
+ "grad_norm": 0.16344796121120453,
1286
+ "learning_rate": 0.000135180412371134,
1287
+ "loss": 0.8569498062133789,
1288
+ "mean_token_accuracy": 0.784570574760437,
1289
+ "num_tokens": 3292457.0,
1290
+ "step": 1280
1291
+ },
1292
+ {
1293
+ "entropy": 0.9115039288997651,
1294
+ "epoch": 1.1082474226804124,
1295
+ "grad_norm": 0.16120180487632751,
1296
+ "learning_rate": 0.00013389175257731958,
1297
+ "loss": 0.9172770500183105,
1298
+ "mean_token_accuracy": 0.7738337874412536,
1299
+ "num_tokens": 3319975.0,
1300
+ "step": 1290
1301
+ },
1302
+ {
1303
+ "entropy": 0.9116923689842225,
1304
+ "epoch": 1.1168384879725086,
1305
+ "grad_norm": 0.1507127285003662,
1306
+ "learning_rate": 0.00013260309278350513,
1307
+ "loss": 0.9115254402160644,
1308
+ "mean_token_accuracy": 0.7727989673614502,
1309
+ "num_tokens": 3349370.0,
1310
+ "step": 1300
1311
+ },
1312
+ {
1313
+ "entropy": 0.8705029428005219,
1314
+ "epoch": 1.1254295532646048,
1315
+ "grad_norm": 0.16798697412014008,
1316
+ "learning_rate": 0.0001313144329896907,
1317
+ "loss": 0.8673007011413574,
1318
+ "mean_token_accuracy": 0.7829907596111297,
1319
+ "num_tokens": 3376086.0,
1320
+ "step": 1310
1321
+ },
1322
+ {
1323
+ "entropy": 0.854372763633728,
1324
+ "epoch": 1.134020618556701,
1325
+ "grad_norm": 0.17355380952358246,
1326
+ "learning_rate": 0.00013002577319587628,
1327
+ "loss": 0.8472127914428711,
1328
+ "mean_token_accuracy": 0.783273845911026,
1329
+ "num_tokens": 3402983.0,
1330
+ "step": 1320
1331
+ },
1332
+ {
1333
+ "entropy": 0.8452303171157837,
1334
+ "epoch": 1.1426116838487972,
1335
+ "grad_norm": 0.17868086695671082,
1336
+ "learning_rate": 0.00012873711340206183,
1337
+ "loss": 0.846036434173584,
1338
+ "mean_token_accuracy": 0.781141072511673,
1339
+ "num_tokens": 3430354.0,
1340
+ "step": 1330
1341
+ },
1342
+ {
1343
+ "entropy": 0.8574681997299194,
1344
+ "epoch": 1.1512027491408934,
1345
+ "grad_norm": 0.17731839418411255,
1346
+ "learning_rate": 0.0001274484536082474,
1347
+ "loss": 0.8377547264099121,
1348
+ "mean_token_accuracy": 0.7869145214557648,
1349
+ "num_tokens": 3456008.0,
1350
+ "step": 1340
1351
+ },
1352
+ {
1353
+ "entropy": 0.8843120455741882,
1354
+ "epoch": 1.1597938144329896,
1355
+ "grad_norm": 0.17710870504379272,
1356
+ "learning_rate": 0.00012615979381443298,
1357
+ "loss": 0.894843578338623,
1358
+ "mean_token_accuracy": 0.7717236757278443,
1359
+ "num_tokens": 3481183.0,
1360
+ "step": 1350
1361
+ },
1362
+ {
1363
+ "entropy": 0.82523393034935,
1364
+ "epoch": 1.168384879725086,
1365
+ "grad_norm": 0.1979239135980606,
1366
+ "learning_rate": 0.00012487113402061855,
1367
+ "loss": 0.8107866287231446,
1368
+ "mean_token_accuracy": 0.7911741256713867,
1369
+ "num_tokens": 3508453.0,
1370
+ "step": 1360
1371
+ },
1372
+ {
1373
+ "entropy": 0.7964858949184418,
1374
+ "epoch": 1.1769759450171822,
1375
+ "grad_norm": 0.14254362881183624,
1376
+ "learning_rate": 0.0001235824742268041,
1377
+ "loss": 0.7863273620605469,
1378
+ "mean_token_accuracy": 0.7948981881141662,
1379
+ "num_tokens": 3534922.0,
1380
+ "step": 1370
1381
+ },
1382
+ {
1383
+ "entropy": 0.8244574010372162,
1384
+ "epoch": 1.1855670103092784,
1385
+ "grad_norm": 0.17570418119430542,
1386
+ "learning_rate": 0.00012229381443298968,
1387
+ "loss": 0.8179922103881836,
1388
+ "mean_token_accuracy": 0.7896173536777497,
1389
+ "num_tokens": 3559367.0,
1390
+ "step": 1380
1391
+ },
1392
+ {
1393
+ "entropy": 0.8134994566440582,
1394
+ "epoch": 1.1941580756013745,
1395
+ "grad_norm": 0.1930961012840271,
1396
+ "learning_rate": 0.00012100515463917526,
1397
+ "loss": 0.8178740501403808,
1398
+ "mean_token_accuracy": 0.7915168821811676,
1399
+ "num_tokens": 3585435.0,
1400
+ "step": 1390
1401
+ },
1402
+ {
1403
+ "entropy": 0.8384514689445496,
1404
+ "epoch": 1.2027491408934707,
1405
+ "grad_norm": 0.17272983491420746,
1406
+ "learning_rate": 0.00011971649484536082,
1407
+ "loss": 0.8454058647155762,
1408
+ "mean_token_accuracy": 0.7868218302726746,
1409
+ "num_tokens": 3613232.0,
1410
+ "step": 1400
1411
+ },
1412
+ {
1413
+ "entropy": 0.9280701756477356,
1414
+ "epoch": 1.211340206185567,
1415
+ "grad_norm": 0.1684003472328186,
1416
+ "learning_rate": 0.00011842783505154638,
1417
+ "loss": 0.9219778060913086,
1418
+ "mean_token_accuracy": 0.7722154259681702,
1419
+ "num_tokens": 3641457.0,
1420
+ "step": 1410
1421
+ },
1422
+ {
1423
+ "entropy": 0.832447475194931,
1424
+ "epoch": 1.2199312714776633,
1425
+ "grad_norm": 0.1773725152015686,
1426
+ "learning_rate": 0.00011713917525773194,
1427
+ "loss": 0.8198453903198242,
1428
+ "mean_token_accuracy": 0.7914207100868225,
1429
+ "num_tokens": 3666482.0,
1430
+ "step": 1420
1431
+ },
1432
+ {
1433
+ "entropy": 0.8738070428371429,
1434
+ "epoch": 1.2285223367697595,
1435
+ "grad_norm": 0.18506592512130737,
1436
+ "learning_rate": 0.00011585051546391752,
1437
+ "loss": 0.8723980903625488,
1438
+ "mean_token_accuracy": 0.778689730167389,
1439
+ "num_tokens": 3692349.0,
1440
+ "step": 1430
1441
+ },
1442
+ {
1443
+ "entropy": 0.8338236033916473,
1444
+ "epoch": 1.2371134020618557,
1445
+ "grad_norm": 0.17101170122623444,
1446
+ "learning_rate": 0.00011456185567010308,
1447
+ "loss": 0.837257194519043,
1448
+ "mean_token_accuracy": 0.7857484579086303,
1449
+ "num_tokens": 3717280.0,
1450
+ "step": 1440
1451
+ },
1452
+ {
1453
+ "entropy": 0.8157192528247833,
1454
+ "epoch": 1.245704467353952,
1455
+ "grad_norm": 0.15433582663536072,
1456
+ "learning_rate": 0.00011327319587628864,
1457
+ "loss": 0.8053474426269531,
1458
+ "mean_token_accuracy": 0.7934693098068237,
1459
+ "num_tokens": 3742595.0,
1460
+ "step": 1450
1461
+ },
1462
+ {
1463
+ "entropy": 0.8770197212696076,
1464
+ "epoch": 1.254295532646048,
1465
+ "grad_norm": 0.17446650564670563,
1466
+ "learning_rate": 0.00011198453608247422,
1467
+ "loss": 0.880764389038086,
1468
+ "mean_token_accuracy": 0.7784990787506103,
1469
+ "num_tokens": 3767913.0,
1470
+ "step": 1460
1471
+ },
1472
+ {
1473
+ "entropy": 0.8850863575935364,
1474
+ "epoch": 1.2628865979381443,
1475
+ "grad_norm": 0.186509370803833,
1476
+ "learning_rate": 0.0001106958762886598,
1477
+ "loss": 0.870394229888916,
1478
+ "mean_token_accuracy": 0.7804449439048767,
1479
+ "num_tokens": 3792206.0,
1480
+ "step": 1470
1481
+ },
1482
+ {
1483
+ "entropy": 0.8432760655879974,
1484
+ "epoch": 1.2714776632302405,
1485
+ "grad_norm": 0.17969489097595215,
1486
+ "learning_rate": 0.00010940721649484534,
1487
+ "loss": 0.8241963386535645,
1488
+ "mean_token_accuracy": 0.7888677537441253,
1489
+ "num_tokens": 3818423.0,
1490
+ "step": 1480
1491
+ },
1492
+ {
1493
+ "entropy": 0.8146856188774109,
1494
+ "epoch": 1.2800687285223367,
1495
+ "grad_norm": 0.15870600938796997,
1496
+ "learning_rate": 0.00010811855670103092,
1497
+ "loss": 0.8046764373779297,
1498
+ "mean_token_accuracy": 0.7905527114868164,
1499
+ "num_tokens": 3844217.0,
1500
+ "step": 1490
1501
+ },
1502
+ {
1503
+ "entropy": 0.8613445937633515,
1504
+ "epoch": 1.2886597938144329,
1505
+ "grad_norm": 0.16997215151786804,
1506
+ "learning_rate": 0.00010682989690721648,
1507
+ "loss": 0.8584735870361329,
1508
+ "mean_token_accuracy": 0.7848546147346497,
1509
+ "num_tokens": 3873420.0,
1510
+ "step": 1500
1511
+ },
1512
+ {
1513
+ "entropy": 0.8456706523895263,
1514
+ "epoch": 1.2972508591065293,
1515
+ "grad_norm": 0.17711228132247925,
1516
+ "learning_rate": 0.00010554123711340206,
1517
+ "loss": 0.8444679260253907,
1518
+ "mean_token_accuracy": 0.7832227885723114,
1519
+ "num_tokens": 3899136.0,
1520
+ "step": 1510
1521
+ },
1522
+ {
1523
+ "entropy": 0.8241195738315582,
1524
+ "epoch": 1.3058419243986255,
1525
+ "grad_norm": 0.1689673215150833,
1526
+ "learning_rate": 0.00010425257731958762,
1527
+ "loss": 0.8167056083679199,
1528
+ "mean_token_accuracy": 0.7913166880607605,
1529
+ "num_tokens": 3924700.0,
1530
+ "step": 1520
1531
+ },
1532
+ {
1533
+ "entropy": 0.7866684377193451,
1534
+ "epoch": 1.3144329896907216,
1535
+ "grad_norm": 0.2105344831943512,
1536
+ "learning_rate": 0.00010296391752577318,
1537
+ "loss": 0.7983303546905518,
1538
+ "mean_token_accuracy": 0.7952485740184784,
1539
+ "num_tokens": 3950430.0,
1540
+ "step": 1530
1541
+ },
1542
+ {
1543
+ "entropy": 0.8364840149879456,
1544
+ "epoch": 1.3230240549828178,
1545
+ "grad_norm": 0.16485024988651276,
1546
+ "learning_rate": 0.00010167525773195876,
1547
+ "loss": 0.8393770217895508,
1548
+ "mean_token_accuracy": 0.7850684821605682,
1549
+ "num_tokens": 3974488.0,
1550
+ "step": 1540
1551
+ },
1552
+ {
1553
+ "entropy": 0.8457872688770294,
1554
+ "epoch": 1.331615120274914,
1555
+ "grad_norm": 0.1678028106689453,
1556
+ "learning_rate": 0.00010038659793814431,
1557
+ "loss": 0.8469937324523926,
1558
+ "mean_token_accuracy": 0.7852873146533966,
1559
+ "num_tokens": 3999599.0,
1560
+ "step": 1550
1561
+ },
1562
+ {
1563
+ "entropy": 0.8001497447490692,
1564
+ "epoch": 1.3402061855670104,
1565
+ "grad_norm": 0.18752388656139374,
1566
+ "learning_rate": 9.909793814432988e-05,
1567
+ "loss": 0.802821159362793,
1568
+ "mean_token_accuracy": 0.7936214208602905,
1569
+ "num_tokens": 4024485.0,
1570
+ "step": 1560
1571
+ },
1572
+ {
1573
+ "entropy": 0.7805682122707367,
1574
+ "epoch": 1.3487972508591066,
1575
+ "grad_norm": 0.18578168749809265,
1576
+ "learning_rate": 9.780927835051546e-05,
1577
+ "loss": 0.7720298767089844,
1578
+ "mean_token_accuracy": 0.7950874090194702,
1579
+ "num_tokens": 4046854.0,
1580
+ "step": 1570
1581
+ },
1582
+ {
1583
+ "entropy": 0.8566708385944366,
1584
+ "epoch": 1.3573883161512028,
1585
+ "grad_norm": 0.20412200689315796,
1586
+ "learning_rate": 9.652061855670104e-05,
1587
+ "loss": 0.8537209510803223,
1588
+ "mean_token_accuracy": 0.7804512619972229,
1589
+ "num_tokens": 4072069.0,
1590
+ "step": 1580
1591
+ },
1592
+ {
1593
+ "entropy": 0.8159787476062774,
1594
+ "epoch": 1.365979381443299,
1595
+ "grad_norm": 0.20074458420276642,
1596
+ "learning_rate": 9.523195876288658e-05,
1597
+ "loss": 0.8033108711242676,
1598
+ "mean_token_accuracy": 0.7907186150550842,
1599
+ "num_tokens": 4098259.0,
1600
+ "step": 1590
1601
+ },
1602
+ {
1603
+ "entropy": 0.885843676328659,
1604
+ "epoch": 1.3745704467353952,
1605
+ "grad_norm": 0.1880599558353424,
1606
+ "learning_rate": 9.394329896907216e-05,
1607
+ "loss": 0.8871627807617187,
1608
+ "mean_token_accuracy": 0.7741815447807312,
1609
+ "num_tokens": 4124396.0,
1610
+ "step": 1600
1611
+ },
1612
+ {
1613
+ "entropy": 0.7828668534755707,
1614
+ "epoch": 1.3831615120274914,
1615
+ "grad_norm": 0.16421489417552948,
1616
+ "learning_rate": 9.265463917525772e-05,
1617
+ "loss": 0.7735602855682373,
1618
+ "mean_token_accuracy": 0.7981120824813843,
1619
+ "num_tokens": 4151812.0,
1620
+ "step": 1610
1621
+ },
1622
+ {
1623
+ "entropy": 0.869307404756546,
1624
+ "epoch": 1.3917525773195876,
1625
+ "grad_norm": 0.15142889320850372,
1626
+ "learning_rate": 9.136597938144328e-05,
1627
+ "loss": 0.8756357192993164,
1628
+ "mean_token_accuracy": 0.7807520747184753,
1629
+ "num_tokens": 4177965.0,
1630
+ "step": 1620
1631
+ },
1632
+ {
1633
+ "entropy": 0.888411694765091,
1634
+ "epoch": 1.4003436426116838,
1635
+ "grad_norm": 0.19432614743709564,
1636
+ "learning_rate": 9.007731958762886e-05,
1637
+ "loss": 0.8830459594726563,
1638
+ "mean_token_accuracy": 0.7777862966060638,
1639
+ "num_tokens": 4202779.0,
1640
+ "step": 1630
1641
+ },
1642
+ {
1643
+ "entropy": 0.7828814208507537,
1644
+ "epoch": 1.40893470790378,
1645
+ "grad_norm": 0.18378111720085144,
1646
+ "learning_rate": 8.878865979381442e-05,
1647
+ "loss": 0.7943001747131347,
1648
+ "mean_token_accuracy": 0.7928287029266358,
1649
+ "num_tokens": 4227115.0,
1650
+ "step": 1640
1651
+ },
1652
+ {
1653
+ "entropy": 0.8880654036998749,
1654
+ "epoch": 1.4175257731958764,
1655
+ "grad_norm": 0.18914829194545746,
1656
+ "learning_rate": 8.75e-05,
1657
+ "loss": 0.8926235198974609,
1658
+ "mean_token_accuracy": 0.7749776244163513,
1659
+ "num_tokens": 4253321.0,
1660
+ "step": 1650
1661
+ },
1662
+ {
1663
+ "entropy": 0.8914766550064087,
1664
+ "epoch": 1.4261168384879725,
1665
+ "grad_norm": 0.17971955239772797,
1666
+ "learning_rate": 8.621134020618555e-05,
1667
+ "loss": 0.8880870819091797,
1668
+ "mean_token_accuracy": 0.7773807585239411,
1669
+ "num_tokens": 4278329.0,
1670
+ "step": 1660
1671
+ },
1672
+ {
1673
+ "entropy": 0.8003105163574219,
1674
+ "epoch": 1.4347079037800687,
1675
+ "grad_norm": 0.17955686151981354,
1676
+ "learning_rate": 8.492268041237112e-05,
1677
+ "loss": 0.7776397705078125,
1678
+ "mean_token_accuracy": 0.7956097960472107,
1679
+ "num_tokens": 4304103.0,
1680
+ "step": 1670
1681
+ },
1682
+ {
1683
+ "entropy": 0.9243861019611359,
1684
+ "epoch": 1.443298969072165,
1685
+ "grad_norm": 0.1783088743686676,
1686
+ "learning_rate": 8.36340206185567e-05,
1687
+ "loss": 0.9214775085449218,
1688
+ "mean_token_accuracy": 0.7736267566680908,
1689
+ "num_tokens": 4331574.0,
1690
+ "step": 1680
1691
+ },
1692
+ {
1693
+ "entropy": 0.822593355178833,
1694
+ "epoch": 1.4518900343642611,
1695
+ "grad_norm": 0.15796494483947754,
1696
+ "learning_rate": 8.234536082474226e-05,
1697
+ "loss": 0.8136516571044922,
1698
+ "mean_token_accuracy": 0.7927704989910126,
1699
+ "num_tokens": 4356400.0,
1700
+ "step": 1690
1701
+ },
1702
+ {
1703
+ "entropy": 0.7921082556247712,
1704
+ "epoch": 1.4604810996563573,
1705
+ "grad_norm": 0.15428173542022705,
1706
+ "learning_rate": 8.105670103092782e-05,
1707
+ "loss": 0.799358606338501,
1708
+ "mean_token_accuracy": 0.7963292002677917,
1709
+ "num_tokens": 4380067.0,
1710
+ "step": 1700
1711
+ },
1712
+ {
1713
+ "entropy": 0.8694933772087097,
1714
+ "epoch": 1.4690721649484537,
1715
+ "grad_norm": 0.15096421539783478,
1716
+ "learning_rate": 7.97680412371134e-05,
1717
+ "loss": 0.8806812286376953,
1718
+ "mean_token_accuracy": 0.7833354890346527,
1719
+ "num_tokens": 4406454.0,
1720
+ "step": 1710
1721
+ },
1722
+ {
1723
+ "entropy": 0.9037382245063782,
1724
+ "epoch": 1.47766323024055,
1725
+ "grad_norm": 0.14613334834575653,
1726
+ "learning_rate": 7.847938144329896e-05,
1727
+ "loss": 0.9070594787597657,
1728
+ "mean_token_accuracy": 0.7784084975719452,
1729
+ "num_tokens": 4433753.0,
1730
+ "step": 1720
1731
+ },
1732
+ {
1733
+ "entropy": 0.8713727951049804,
1734
+ "epoch": 1.486254295532646,
1735
+ "grad_norm": 0.18062028288841248,
1736
+ "learning_rate": 7.719072164948453e-05,
1737
+ "loss": 0.8788843154907227,
1738
+ "mean_token_accuracy": 0.7776244163513184,
1739
+ "num_tokens": 4458675.0,
1740
+ "step": 1730
1741
+ },
1742
+ {
1743
+ "entropy": 0.8470430076122284,
1744
+ "epoch": 1.4948453608247423,
1745
+ "grad_norm": 0.17912915349006653,
1746
+ "learning_rate": 7.590206185567009e-05,
1747
+ "loss": 0.8447764396667481,
1748
+ "mean_token_accuracy": 0.7814169287681579,
1749
+ "num_tokens": 4484460.0,
1750
+ "step": 1740
1751
+ },
1752
+ {
1753
+ "entropy": 0.8573691666126251,
1754
+ "epoch": 1.5034364261168385,
1755
+ "grad_norm": 0.19001024961471558,
1756
+ "learning_rate": 7.461340206185566e-05,
1757
+ "loss": 0.8481529235839844,
1758
+ "mean_token_accuracy": 0.7841818153858184,
1759
+ "num_tokens": 4509367.0,
1760
+ "step": 1750
1761
+ },
1762
+ {
1763
+ "entropy": 0.812692153453827,
1764
+ "epoch": 1.5120274914089347,
1765
+ "grad_norm": 0.14844155311584473,
1766
+ "learning_rate": 7.332474226804123e-05,
1767
+ "loss": 0.7989925384521485,
1768
+ "mean_token_accuracy": 0.7950545191764832,
1769
+ "num_tokens": 4534267.0,
1770
+ "step": 1760
1771
+ },
1772
+ {
1773
+ "entropy": 0.8320570230484009,
1774
+ "epoch": 1.5206185567010309,
1775
+ "grad_norm": 0.15702161192893982,
1776
+ "learning_rate": 7.20360824742268e-05,
1777
+ "loss": 0.8325714111328125,
1778
+ "mean_token_accuracy": 0.7877066612243653,
1779
+ "num_tokens": 4560969.0,
1780
+ "step": 1770
1781
+ },
1782
+ {
1783
+ "entropy": 0.8388019800186157,
1784
+ "epoch": 1.529209621993127,
1785
+ "grad_norm": 0.18410871922969818,
1786
+ "learning_rate": 7.074742268041236e-05,
1787
+ "loss": 0.8359848976135253,
1788
+ "mean_token_accuracy": 0.7866321623325347,
1789
+ "num_tokens": 4588280.0,
1790
+ "step": 1780
1791
+ },
1792
+ {
1793
+ "entropy": 0.8161091566085815,
1794
+ "epoch": 1.5378006872852232,
1795
+ "grad_norm": 0.17120403051376343,
1796
+ "learning_rate": 6.945876288659794e-05,
1797
+ "loss": 0.7916454315185547,
1798
+ "mean_token_accuracy": 0.7996809184551239,
1799
+ "num_tokens": 4613497.0,
1800
+ "step": 1790
1801
+ },
1802
+ {
1803
+ "entropy": 0.7915269613265992,
1804
+ "epoch": 1.5463917525773194,
1805
+ "grad_norm": 0.1651919186115265,
1806
+ "learning_rate": 6.81701030927835e-05,
1807
+ "loss": 0.7937773704528809,
1808
+ "mean_token_accuracy": 0.7961802840232849,
1809
+ "num_tokens": 4638371.0,
1810
+ "step": 1800
1811
+ },
1812
+ {
1813
+ "entropy": 0.8150420367717743,
1814
+ "epoch": 1.5549828178694158,
1815
+ "grad_norm": 0.17061056196689606,
1816
+ "learning_rate": 6.688144329896906e-05,
1817
+ "loss": 0.8088832855224609,
1818
+ "mean_token_accuracy": 0.7914064228534698,
1819
+ "num_tokens": 4663692.0,
1820
+ "step": 1810
1821
+ },
1822
+ {
1823
+ "entropy": 0.7606650114059448,
1824
+ "epoch": 1.563573883161512,
1825
+ "grad_norm": 0.20968209207057953,
1826
+ "learning_rate": 6.559278350515463e-05,
1827
+ "loss": 0.753024673461914,
1828
+ "mean_token_accuracy": 0.8017209947109223,
1829
+ "num_tokens": 4688045.0,
1830
+ "step": 1820
1831
+ },
1832
+ {
1833
+ "entropy": 0.8470539689064026,
1834
+ "epoch": 1.5721649484536082,
1835
+ "grad_norm": 0.19549283385276794,
1836
+ "learning_rate": 6.43041237113402e-05,
1837
+ "loss": 0.8378950119018554,
1838
+ "mean_token_accuracy": 0.785133320093155,
1839
+ "num_tokens": 4713328.0,
1840
+ "step": 1830
1841
+ },
1842
+ {
1843
+ "entropy": 0.8347568809986115,
1844
+ "epoch": 1.5807560137457046,
1845
+ "grad_norm": 0.19542083144187927,
1846
+ "learning_rate": 6.301546391752577e-05,
1847
+ "loss": 0.8309958457946778,
1848
+ "mean_token_accuracy": 0.7830386936664582,
1849
+ "num_tokens": 4736645.0,
1850
+ "step": 1840
1851
+ },
1852
+ {
1853
+ "entropy": 0.8486950516700744,
1854
+ "epoch": 1.5893470790378008,
1855
+ "grad_norm": 0.17605172097682953,
1856
+ "learning_rate": 6.172680412371133e-05,
1857
+ "loss": 0.8492973327636719,
1858
+ "mean_token_accuracy": 0.7818349421024322,
1859
+ "num_tokens": 4762593.0,
1860
+ "step": 1850
1861
+ },
1862
+ {
1863
+ "entropy": 0.8633189976215363,
1864
+ "epoch": 1.597938144329897,
1865
+ "grad_norm": 0.1746501475572586,
1866
+ "learning_rate": 6.0438144329896904e-05,
1867
+ "loss": 0.8715569496154785,
1868
+ "mean_token_accuracy": 0.7832526326179504,
1869
+ "num_tokens": 4790512.0,
1870
+ "step": 1860
1871
+ },
1872
+ {
1873
+ "entropy": 0.8331901133060455,
1874
+ "epoch": 1.6065292096219932,
1875
+ "grad_norm": 0.18069683015346527,
1876
+ "learning_rate": 5.9149484536082466e-05,
1877
+ "loss": 0.8255064010620117,
1878
+ "mean_token_accuracy": 0.7909268379211426,
1879
+ "num_tokens": 4815026.0,
1880
+ "step": 1870
1881
+ },
1882
+ {
1883
+ "entropy": 0.8436095595359803,
1884
+ "epoch": 1.6151202749140894,
1885
+ "grad_norm": 0.17736031115055084,
1886
+ "learning_rate": 5.786082474226804e-05,
1887
+ "loss": 0.8569248199462891,
1888
+ "mean_token_accuracy": 0.7808656811714172,
1889
+ "num_tokens": 4839215.0,
1890
+ "step": 1880
1891
+ },
1892
+ {
1893
+ "entropy": 0.7866667926311492,
1894
+ "epoch": 1.6237113402061856,
1895
+ "grad_norm": 0.17926126718521118,
1896
+ "learning_rate": 5.6572164948453604e-05,
1897
+ "loss": 0.7782703399658203,
1898
+ "mean_token_accuracy": 0.79602530002594,
1899
+ "num_tokens": 4862402.0,
1900
+ "step": 1890
1901
+ },
1902
+ {
1903
+ "entropy": 0.8086029171943665,
1904
+ "epoch": 1.6323024054982818,
1905
+ "grad_norm": 0.18922077119350433,
1906
+ "learning_rate": 5.5283505154639174e-05,
1907
+ "loss": 0.793773603439331,
1908
+ "mean_token_accuracy": 0.7902358531951904,
1909
+ "num_tokens": 4884961.0,
1910
+ "step": 1900
1911
+ },
1912
+ {
1913
+ "entropy": 0.8036526799201965,
1914
+ "epoch": 1.640893470790378,
1915
+ "grad_norm": 0.16250303387641907,
1916
+ "learning_rate": 5.3994845360824736e-05,
1917
+ "loss": 0.8118386268615723,
1918
+ "mean_token_accuracy": 0.7943076312541961,
1919
+ "num_tokens": 4910669.0,
1920
+ "step": 1910
1921
+ },
1922
+ {
1923
+ "entropy": 0.8436183631420135,
1924
+ "epoch": 1.6494845360824741,
1925
+ "grad_norm": 0.20115436613559723,
1926
+ "learning_rate": 5.27061855670103e-05,
1927
+ "loss": 0.8377361297607422,
1928
+ "mean_token_accuracy": 0.7822086751461029,
1929
+ "num_tokens": 4936469.0,
1930
+ "step": 1920
1931
+ },
1932
+ {
1933
+ "entropy": 0.8387295484542847,
1934
+ "epoch": 1.6580756013745703,
1935
+ "grad_norm": 0.18081411719322205,
1936
+ "learning_rate": 5.1417525773195874e-05,
1937
+ "loss": 0.8227550506591796,
1938
+ "mean_token_accuracy": 0.7908512234687806,
1939
+ "num_tokens": 4961585.0,
1940
+ "step": 1930
1941
+ },
1942
+ {
1943
+ "entropy": 0.827156925201416,
1944
+ "epoch": 1.6666666666666665,
1945
+ "grad_norm": 0.16519112884998322,
1946
+ "learning_rate": 5.012886597938144e-05,
1947
+ "loss": 0.8146243095397949,
1948
+ "mean_token_accuracy": 0.7934521496295929,
1949
+ "num_tokens": 4986973.0,
1950
+ "step": 1940
1951
+ },
1952
+ {
1953
+ "entropy": 0.8766247212886811,
1954
+ "epoch": 1.675257731958763,
1955
+ "grad_norm": 0.18259386718273163,
1956
+ "learning_rate": 4.8840206185567006e-05,
1957
+ "loss": 0.9040776252746582,
1958
+ "mean_token_accuracy": 0.7768758893013,
1959
+ "num_tokens": 5015328.0,
1960
+ "step": 1950
1961
+ },
1962
+ {
1963
+ "entropy": 0.8589329719543457,
1964
+ "epoch": 1.6838487972508591,
1965
+ "grad_norm": 0.17805488407611847,
1966
+ "learning_rate": 4.755154639175257e-05,
1967
+ "loss": 0.843138599395752,
1968
+ "mean_token_accuracy": 0.7871474087238312,
1969
+ "num_tokens": 5040331.0,
1970
+ "step": 1960
1971
+ },
1972
+ {
1973
+ "entropy": 0.8477513492107391,
1974
+ "epoch": 1.6924398625429553,
1975
+ "grad_norm": 0.1912812739610672,
1976
+ "learning_rate": 4.6262886597938144e-05,
1977
+ "loss": 0.8690207481384278,
1978
+ "mean_token_accuracy": 0.7804648995399475,
1979
+ "num_tokens": 5067476.0,
1980
+ "step": 1970
1981
+ },
1982
+ {
1983
+ "entropy": 0.839428323507309,
1984
+ "epoch": 1.7010309278350515,
1985
+ "grad_norm": 0.17795996367931366,
1986
+ "learning_rate": 4.4974226804123706e-05,
1987
+ "loss": 0.8250746726989746,
1988
+ "mean_token_accuracy": 0.7934922575950623,
1989
+ "num_tokens": 5093307.0,
1990
+ "step": 1980
1991
+ },
1992
+ {
1993
+ "entropy": 0.8613415479660034,
1994
+ "epoch": 1.709621993127148,
1995
+ "grad_norm": 0.16119547188282013,
1996
+ "learning_rate": 4.3685567010309276e-05,
1997
+ "loss": 0.8428993225097656,
1998
+ "mean_token_accuracy": 0.7848868429660797,
1999
+ "num_tokens": 5119275.0,
2000
+ "step": 1990
2001
+ },
2002
+ {
2003
+ "entropy": 0.916008472442627,
2004
+ "epoch": 1.718213058419244,
2005
+ "grad_norm": 0.17801715433597565,
2006
+ "learning_rate": 4.239690721649484e-05,
2007
+ "loss": 0.9225644111633301,
2008
+ "mean_token_accuracy": 0.7754484415054321,
2009
+ "num_tokens": 5145206.0,
2010
+ "step": 2000
2011
+ }
2012
+ ],
2013
+ "logging_steps": 10,
2014
+ "max_steps": 2328,
2015
+ "num_input_tokens_seen": 0,
2016
+ "num_train_epochs": 2,
2017
+ "save_steps": 500,
2018
+ "stateful_callbacks": {
2019
+ "TrainerControl": {
2020
+ "args": {
2021
+ "should_epoch_stop": false,
2022
+ "should_evaluate": false,
2023
+ "should_log": false,
2024
+ "should_save": true,
2025
+ "should_training_stop": false
2026
+ },
2027
+ "attributes": {}
2028
+ }
2029
+ },
2030
+ "total_flos": 8417798278571520.0,
2031
+ "train_batch_size": 16,
2032
+ "trial_name": null,
2033
+ "trial_params": null
2034
+ }
checkpoint-2000/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f2af45870b4c4d611381846df6bdd4a32cb292a7b5897b79b7e5f196be57c596
3
+ size 5585
checkpoint-2328/README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: HuggingFaceTB/SmolLM2-135M-Instruct
3
+ library_name: peft
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - base_model:adapter:HuggingFaceTB/SmolLM2-135M-Instruct
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ ---
12
+
13
+ # Model Card for Model ID
14
+
15
+ <!-- Provide a quick summary of what the model is/does. -->
16
+
17
+
18
+
19
+ ## Model Details
20
+
21
+ ### Model Description
22
+
23
+ <!-- Provide a longer summary of what this model is. -->
24
+
25
+
26
+
27
+ - **Developed by:** [More Information Needed]
28
+ - **Funded by [optional]:** [More Information Needed]
29
+ - **Shared by [optional]:** [More Information Needed]
30
+ - **Model type:** [More Information Needed]
31
+ - **Language(s) (NLP):** [More Information Needed]
32
+ - **License:** [More Information Needed]
33
+ - **Finetuned from model [optional]:** [More Information Needed]
34
+
35
+ ### Model Sources [optional]
36
+
37
+ <!-- Provide the basic links for the model. -->
38
+
39
+ - **Repository:** [More Information Needed]
40
+ - **Paper [optional]:** [More Information Needed]
41
+ - **Demo [optional]:** [More Information Needed]
42
+
43
+ ## Uses
44
+
45
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
46
+
47
+ ### Direct Use
48
+
49
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
50
+
51
+ [More Information Needed]
52
+
53
+ ### Downstream Use [optional]
54
+
55
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
56
+
57
+ [More Information Needed]
58
+
59
+ ### Out-of-Scope Use
60
+
61
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
62
+
63
+ [More Information Needed]
64
+
65
+ ## Bias, Risks, and Limitations
66
+
67
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
68
+
69
+ [More Information Needed]
70
+
71
+ ### Recommendations
72
+
73
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
74
+
75
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
76
+
77
+ ## How to Get Started with the Model
78
+
79
+ Use the code below to get started with the model.
80
+
81
+ [More Information Needed]
82
+
83
+ ## Training Details
84
+
85
+ ### Training Data
86
+
87
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
88
+
89
+ [More Information Needed]
90
+
91
+ ### Training Procedure
92
+
93
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
94
+
95
+ #### Preprocessing [optional]
96
+
97
+ [More Information Needed]
98
+
99
+
100
+ #### Training Hyperparameters
101
+
102
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
103
+
104
+ #### Speeds, Sizes, Times [optional]
105
+
106
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
107
+
108
+ [More Information Needed]
109
+
110
+ ## Evaluation
111
+
112
+ <!-- This section describes the evaluation protocols and provides the results. -->
113
+
114
+ ### Testing Data, Factors & Metrics
115
+
116
+ #### Testing Data
117
+
118
+ <!-- This should link to a Dataset Card if possible. -->
119
+
120
+ [More Information Needed]
121
+
122
+ #### Factors
123
+
124
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
125
+
126
+ [More Information Needed]
127
+
128
+ #### Metrics
129
+
130
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
131
+
132
+ [More Information Needed]
133
+
134
+ ### Results
135
+
136
+ [More Information Needed]
137
+
138
+ #### Summary
139
+
140
+
141
+
142
+ ## Model Examination [optional]
143
+
144
+ <!-- Relevant interpretability work for the model goes here -->
145
+
146
+ [More Information Needed]
147
+
148
+ ## Environmental Impact
149
+
150
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
151
+
152
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
153
+
154
+ - **Hardware Type:** [More Information Needed]
155
+ - **Hours used:** [More Information Needed]
156
+ - **Cloud Provider:** [More Information Needed]
157
+ - **Compute Region:** [More Information Needed]
158
+ - **Carbon Emitted:** [More Information Needed]
159
+
160
+ ## Technical Specifications [optional]
161
+
162
+ ### Model Architecture and Objective
163
+
164
+ [More Information Needed]
165
+
166
+ ### Compute Infrastructure
167
+
168
+ [More Information Needed]
169
+
170
+ #### Hardware
171
+
172
+ [More Information Needed]
173
+
174
+ #### Software
175
+
176
+ [More Information Needed]
177
+
178
+ ## Citation [optional]
179
+
180
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
181
+
182
+ **BibTeX:**
183
+
184
+ [More Information Needed]
185
+
186
+ **APA:**
187
+
188
+ [More Information Needed]
189
+
190
+ ## Glossary [optional]
191
+
192
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
193
+
194
+ [More Information Needed]
195
+
196
+ ## More Information [optional]
197
+
198
+ [More Information Needed]
199
+
200
+ ## Model Card Authors [optional]
201
+
202
+ [More Information Needed]
203
+
204
+ ## Model Card Contact
205
+
206
+ [More Information Needed]
207
+ ### Framework versions
208
+
209
+ - PEFT 0.18.1
checkpoint-2328/adapter_config.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alora_invocation_tokens": null,
3
+ "alpha_pattern": {},
4
+ "arrow_config": null,
5
+ "auto_mapping": null,
6
+ "base_model_name_or_path": "HuggingFaceTB/SmolLM2-135M-Instruct",
7
+ "bias": "none",
8
+ "corda_config": null,
9
+ "ensure_weight_tying": false,
10
+ "eva_config": null,
11
+ "exclude_modules": null,
12
+ "fan_in_fan_out": false,
13
+ "inference_mode": true,
14
+ "init_lora_weights": true,
15
+ "layer_replication": null,
16
+ "layers_pattern": null,
17
+ "layers_to_transform": null,
18
+ "loftq_config": {},
19
+ "lora_alpha": 32,
20
+ "lora_bias": false,
21
+ "lora_dropout": 0.05,
22
+ "megatron_config": null,
23
+ "megatron_core": "megatron.core",
24
+ "modules_to_save": null,
25
+ "peft_type": "LORA",
26
+ "peft_version": "0.18.1",
27
+ "qalora_group_size": 16,
28
+ "r": 16,
29
+ "rank_pattern": {},
30
+ "revision": null,
31
+ "target_modules": [
32
+ "o_proj",
33
+ "k_proj",
34
+ "q_proj",
35
+ "v_proj"
36
+ ],
37
+ "target_parameters": null,
38
+ "task_type": "CAUSAL_LM",
39
+ "trainable_token_indices": null,
40
+ "use_dora": false,
41
+ "use_qalora": false,
42
+ "use_rslora": false
43
+ }
checkpoint-2328/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7fc9ea20edd42cffd8c5b32349900876962b89d0cf0b52aa2d24603f2324ff2a
3
+ size 7404368
checkpoint-2328/chat_template.jinja ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
2
+ You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
3
+ ' }}{% endif %}{{'<|im_start|>' + message['role'] + '
4
+ ' + message['content'] + '<|im_end|>' + '
5
+ '}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
6
+ ' }}{% endif %}
checkpoint-2328/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c531d5c0a682388d0333585d06d904c7a8cd92db47c6559ae30bea0d39ab20c9
3
+ size 14950667
checkpoint-2328/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e5673270aeec65920561e25573ba9faecb0ca3976d65cd2f6489d7752232bd40
3
+ size 14645
checkpoint-2328/scaler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:834a2e6b633182438229fc6544de1f73bd698865a519299ffd79b8b9dff673ae
3
+ size 1383
checkpoint-2328/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a491c550d4a09236e07f87e9a8ccc2e31bb1a97085c661c7e88845ead685832c
3
+ size 1465
checkpoint-2328/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-2328/tokenizer_config.json ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
4
+ "bos_token": "<|im_start|>",
5
+ "clean_up_tokenization_spaces": false,
6
+ "eos_token": "<|im_end|>",
7
+ "extra_special_tokens": [
8
+ "<|im_start|>",
9
+ "<|im_end|>"
10
+ ],
11
+ "is_local": false,
12
+ "model_max_length": 8192,
13
+ "pad_token": "<|im_end|>",
14
+ "tokenizer_class": "TokenizersBackend",
15
+ "unk_token": "<|endoftext|>",
16
+ "vocab_size": 49152
17
+ }