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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,235 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ language:
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+ - en
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+ license: apache-2.0
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+ library_name: transformers
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+ tags:
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+ - self-distillation
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+ - ssd
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+ - qwen
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+ - qwen3.6
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+ - moe
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+ - deltanet
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+ - linear-attention
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+ - code-generation
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+ - coding
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+ - lora-merged
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+ - bf16
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+ base_model: Qwen/Qwen3.6-35B-A3B
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+ pipeline_tag: text-generation
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+ model-index:
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+ - name: Qwen3.6-35B-A3B-SSD
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+ results:
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+ - task:
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+ type: text-generation
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+ dataset:
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+ name: Self-generated coding dataset (SSD)
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+ type: custom
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+ metrics:
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+ - name: Train Loss
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+ type: train_loss
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+ value: 0.523
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+ - name: pass@10 (temp=0.7, 13 problems)
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+ type: pass_at_k
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+ value: 0.985
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+ ---
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+
37
+ # Qwen3.6-35B-A3B-SSD
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+
39
+ A self-distilled version of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B), fine-tuned using the **Self-play Self-Distillation (SSD)** technique. The LoRA adapter has been merged into the base weights -- this is a standard bf16 model ready for direct use or quantization.
40
+
41
+ ## What is SSD?
42
+
43
+ Self-play Self-Distillation generates training data from the model itself at high temperature (1.6), filters for correctness, then fine-tunes the model on its own best outputs. No external judge, no reward model, no human annotation required.
44
+
45
+ The key insight: training on the model's own high-temperature outputs acts as a form of self-distillation that improves pass@k at temperature > 0. At temp=0, the merged model produces output identical to base. The improvement appears in the diversity and correctness of samples at higher temperatures.
46
+
47
+ ## Model Details
48
+
49
+ | Property | Value |
50
+ |----------|-------|
51
+ | Architecture | Qwen3.5 MoE with Gated DeltaNet linear attention |
52
+ | Total parameters | 34.66B |
53
+ | Active parameters | ~3B (Mixture of Experts, 256 experts, 8 active per token) |
54
+ | Hidden layers | 40 (30 linear attention + 10 full attention) |
55
+ | Precision | bfloat16 |
56
+ | Model size on disk | ~64 GB |
57
+ | Context length | 262,144 tokens |
58
+ | License | Apache 2.0 |
59
+
60
+ ## Training Details
61
+
62
+ ### Method
63
+
64
+ 1. Generated 2,000 coding solutions from the base model at temp=1.6, top_k=20, top_p=0.8
65
+ 2. Filtered for correctness (execution + test pass) -- 1,796 samples survived
66
+ 3. Split into 1,616 train / 180 validation
67
+ 4. Fine-tuned with LoRA, then merged adapter into base weights
68
+
69
+ ### LoRA Configuration
70
+
71
+ | Parameter | Value |
72
+ |-----------|-------|
73
+ | Rank (r) | 16 |
74
+ | Alpha | 16 |
75
+ | Dropout | 0.0 |
76
+ | Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, in_proj_qkv, in_proj_z, out_proj |
77
+ | Trainable parameters | 19.2M / 34.66B (0.055%) |
78
+
79
+ The target modules include both standard transformer attention/MLP layers and Qwen3.6's DeltaNet linear attention layers (in_proj_qkv, in_proj_z, out_proj).
80
+
81
+ ### Training Hyperparameters
82
+
83
+ | Parameter | Value |
84
+ |-----------|-------|
85
+ | Optimizer | AdamW 8-bit |
86
+ | Learning rate | 2e-4 (cosine schedule) |
87
+ | Warmup | 6% of steps |
88
+ | Max steps | 150 |
89
+ | Batch size | 4 |
90
+ | Gradient accumulation | 8 (effective batch = 32) |
91
+ | Max sequence length | 2,048 |
92
+ | Weight decay | 0.01 |
93
+ | Precision | bfloat16 (no quantization during training) |
94
+ | Seed | 42 |
95
+
96
+ ### Training Results
97
+
98
+ | Metric | Value |
99
+ |--------|-------|
100
+ | Final train loss | 0.523 |
101
+ | Eval loss | 0.482 (at step 150) |
102
+ | Token accuracy | 85.9% |
103
+ | Training time | 78 min |
104
+ | Peak GPU memory | 64.7 GB |
105
+ | Hardware | NVIDIA H200 (Modal cloud) |
106
+ | Estimated cost | ~$6.20 |
107
+
108
+ ### Merge
109
+
110
+ Adapter merged into base weights using `PeftModel.merge_and_unload()` from PEFT 0.19.1. The result is a standard HuggingFace model -- no adapter loading required at inference time.
111
+
112
+ ## Evaluation
113
+
114
+ Tested as a 6-bit MLX quantization on Mac Studio M4 Max (128GB) against the base model (unsloth 4-bit quantization). 13 coding problems, 10 samples each at temp=0.7:
115
+
116
+ | Problem difficulty | Base (4-bit) | Merged (6-bit) |
117
+ |-------------------|-------------|----------------|
118
+ | Easy (5 problems) | 50/50 (100%) | 50/50 (100%) |
119
+ | Hard (8 problems) | 76/80 (95%) | 78/80 (98%) |
120
+ | **Overall** | **126/130 (97%)** | **128/130 (98%)** |
121
+
122
+ Biggest improvement on the hardest problem (expression evaluator with operator precedence and parentheses): base 7/10 → merged 9/10.
123
+
124
+ | Metric | Value |
125
+ |--------|-------|
126
+ | Inference speed (6-bit MLX) | 78.9 tok/s average |
127
+ | Base model speed (4-bit MLX) | 86.7 tok/s average |
128
+
129
+ Note: At temp=0, the merged model produces output identical to the base model. The SSD improvement manifests at temperature > 0 through improved sample diversity and correctness, particularly on harder problems.
130
+
131
+ ## How to Use
132
+
133
+ ### With Transformers
134
+
135
+ ```python
136
+ from transformers import AutoModelForCausalLM, AutoTokenizer
137
+ import torch
138
+
139
+ model = AutoModelForCausalLM.from_pretrained(
140
+ "shaneMattner/Qwen3.6-35B-A3B-SSD",
141
+ torch_dtype=torch.bfloat16,
142
+ device_map="auto",
143
+ attn_implementation="eager",
144
+ )
145
+ tokenizer = AutoTokenizer.from_pretrained("shaneMattner/Qwen3.6-35B-A3B-SSD")
146
+
147
+ messages = [
148
+ {"role": "user", "content": "Write a Python function to merge two sorted lists into one sorted list."}
149
+ ]
150
+ text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
151
+ inputs = tokenizer(text, return_tensors="pt").to(model.device)
152
+ outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
153
+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
154
+ ```
155
+
156
+ ### With MLX (Apple Silicon)
157
+
158
+ ```bash
159
+ pip install mlx-lm
160
+ ```
161
+
162
+ ```python
163
+ from mlx_lm import load, generate
164
+
165
+ model, tokenizer = load("shaneMattner/Qwen3.6-35B-A3B-SSD")
166
+ response = generate(
167
+ model,
168
+ tokenizer,
169
+ prompt="Write a Python function to merge two sorted lists.",
170
+ max_tokens=512,
171
+ )
172
+ print(response)
173
+ ```
174
+
175
+ Or quantize first for faster inference:
176
+
177
+ ```bash
178
+ # Convert to 6-bit MLX format
179
+ python -m mlx_lm.convert \
180
+ --hf-path shaneMattner/Qwen3.6-35B-A3B-SSD \
181
+ --mlx-path Qwen3.6-35B-A3B-SSD-6bit \
182
+ -q --q-bits 6
183
+ ```
184
+
185
+ **Note**: If you encounter errors related to `model_type`, you may need to change `"model_type": "qwen3_5_moe_text"` to `"model_type": "qwen3_5_moe"` in `config.json` for mlx-lm compatibility.
186
+
187
+ ### With llama.cpp / GGUF
188
+
189
+ Convert to GGUF for use with llama.cpp, Ollama, or other GGUF-compatible tools:
190
+
191
+ ```bash
192
+ # Clone llama.cpp and convert
193
+ python convert_hf_to_gguf.py shaneMattner/Qwen3.6-35B-A3B-SSD --outtype bf16
194
+
195
+ # Quantize to desired format
196
+ ./llama-quantize Qwen3.6-35B-A3B-SSD-bf16.gguf Qwen3.6-35B-A3B-SSD-Q4_K_M.gguf Q4_K_M
197
+ ```
198
+
199
+ ## Limitations
200
+
201
+ - **Coding-focused**: Fine-tuned exclusively on Python coding tasks. General instruction following may not improve (or may slightly regress) compared to the base model.
202
+ - **Bounded by base model**: Self-distillation cannot exceed the base model's capability ceiling -- it improves sampling consistency, not peak ability.
203
+ - **Small training set**: 1,616 samples is a proof-of-concept. Larger datasets with more diverse problems would likely yield stronger results.
204
+ - **Eval coverage**: Tested on 5 coding problems only. Broader benchmarks (HumanEval, MBPP, etc.) have not been run.
205
+ - **DeltaNet targeting**: The in_proj_a and in_proj_b DeltaNet gating layers were not included in LoRA targets -- adding them may improve results in future iterations.
206
+
207
+ ## Architecture Notes
208
+
209
+ Qwen3.6-35B-A3B uses a hybrid architecture:
210
+ - **Mixture of Experts (MoE)**: 256 experts with 8 active per token, keeping active compute at ~3B parameters despite 34.66B total
211
+ - **Gated DeltaNet linear attention**: 30 of 40 layers use linear attention (every 4th layer uses full attention), enabling efficient long-context processing
212
+ - **262K context window**: Supports up to 262,144 tokens
213
+
214
+ ## Citation
215
+
216
+ If you use this model, please cite:
217
+
218
+ ```bibtex
219
+ @misc{mattner2026qwen36ssd,
220
+ title={Qwen3.6-35B-A3B-SSD: Self-Distilled Qwen3.6 for Coding},
221
+ author={Shane Mattner},
222
+ year={2026},
223
+ url={https://huggingface.co/shaneMattner/Qwen3.6-35B-A3B-SSD}
224
+ }
225
+ ```
226
+
227
+ ### Related Work
228
+
229
+ - [Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) -- Base model by Qwen team
230
+ - [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) -- Hu et al., 2021
231
+ - Self-play Self-Distillation (SSD) technique for improving pass@k at temperature > 0
232
+
233
+ ## License
234
+
235
+ Apache 2.0 (same as the base model [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B))
chat_template.jinja ADDED
@@ -0,0 +1,154 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- set image_count = namespace(value=0) %}
2
+ {%- set video_count = namespace(value=0) %}
3
+ {%- macro render_content(content, do_vision_count, is_system_content=false) %}
4
+ {%- if content is string %}
5
+ {{- content }}
6
+ {%- elif content is iterable and content is not mapping %}
7
+ {%- for item in content %}
8
+ {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
9
+ {%- if is_system_content %}
10
+ {{- raise_exception('System message cannot contain images.') }}
11
+ {%- endif %}
12
+ {%- if do_vision_count %}
13
+ {%- set image_count.value = image_count.value + 1 %}
14
+ {%- endif %}
15
+ {%- if add_vision_id %}
16
+ {{- 'Picture ' ~ image_count.value ~ ': ' }}
17
+ {%- endif %}
18
+ {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
19
+ {%- elif 'video' in item or item.type == 'video' %}
20
+ {%- if is_system_content %}
21
+ {{- raise_exception('System message cannot contain videos.') }}
22
+ {%- endif %}
23
+ {%- if do_vision_count %}
24
+ {%- set video_count.value = video_count.value + 1 %}
25
+ {%- endif %}
26
+ {%- if add_vision_id %}
27
+ {{- 'Video ' ~ video_count.value ~ ': ' }}
28
+ {%- endif %}
29
+ {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
30
+ {%- elif 'text' in item %}
31
+ {{- item.text }}
32
+ {%- else %}
33
+ {{- raise_exception('Unexpected item type in content.') }}
34
+ {%- endif %}
35
+ {%- endfor %}
36
+ {%- elif content is none or content is undefined %}
37
+ {{- '' }}
38
+ {%- else %}
39
+ {{- raise_exception('Unexpected content type.') }}
40
+ {%- endif %}
41
+ {%- endmacro %}
42
+ {%- if not messages %}
43
+ {{- raise_exception('No messages provided.') }}
44
+ {%- endif %}
45
+ {%- if tools and tools is iterable and tools is not mapping %}
46
+ {{- '<|im_start|>system\n' }}
47
+ {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
48
+ {%- for tool in tools %}
49
+ {{- "\n" }}
50
+ {{- tool | tojson }}
51
+ {%- endfor %}
52
+ {{- "\n</tools>" }}
53
+ {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
54
+ {%- if messages[0].role == 'system' %}
55
+ {%- set content = render_content(messages[0].content, false, true)|trim %}
56
+ {%- if content %}
57
+ {{- '\n\n' + content }}
58
+ {%- endif %}
59
+ {%- endif %}
60
+ {{- '<|im_end|>\n' }}
61
+ {%- else %}
62
+ {%- if messages[0].role == 'system' %}
63
+ {%- set content = render_content(messages[0].content, false, true)|trim %}
64
+ {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
65
+ {%- endif %}
66
+ {%- endif %}
67
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
68
+ {%- for message in messages[::-1] %}
69
+ {%- set index = (messages|length - 1) - loop.index0 %}
70
+ {%- if ns.multi_step_tool and message.role == "user" %}
71
+ {%- set content = render_content(message.content, false)|trim %}
72
+ {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
73
+ {%- set ns.multi_step_tool = false %}
74
+ {%- set ns.last_query_index = index %}
75
+ {%- endif %}
76
+ {%- endif %}
77
+ {%- endfor %}
78
+ {%- if ns.multi_step_tool %}
79
+ {{- raise_exception('No user query found in messages.') }}
80
+ {%- endif %}
81
+ {%- for message in messages %}
82
+ {%- set content = render_content(message.content, true)|trim %}
83
+ {%- if message.role == "system" %}
84
+ {%- if not loop.first %}
85
+ {{- raise_exception('System message must be at the beginning.') }}
86
+ {%- endif %}
87
+ {%- elif message.role == "user" %}
88
+ {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
89
+ {%- elif message.role == "assistant" %}
90
+ {%- set reasoning_content = '' %}
91
+ {%- if message.reasoning_content is string %}
92
+ {%- set reasoning_content = message.reasoning_content %}
93
+ {%- else %}
94
+ {%- if '</think>' in content %}
95
+ {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
96
+ {%- set content = content.split('</think>')[-1].lstrip('\n') %}
97
+ {%- endif %}
98
+ {%- endif %}
99
+ {%- set reasoning_content = reasoning_content|trim %}
100
+ {%- if (preserve_thinking is defined and preserve_thinking is true) or (loop.index0 > ns.last_query_index) %}
101
+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
102
+ {%- else %}
103
+ {{- '<|im_start|>' + message.role + '\n' + content }}
104
+ {%- endif %}
105
+ {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
106
+ {%- for tool_call in message.tool_calls %}
107
+ {%- if tool_call.function is defined %}
108
+ {%- set tool_call = tool_call.function %}
109
+ {%- endif %}
110
+ {%- if loop.first %}
111
+ {%- if content|trim %}
112
+ {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
113
+ {%- else %}
114
+ {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
115
+ {%- endif %}
116
+ {%- else %}
117
+ {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
118
+ {%- endif %}
119
+ {%- if tool_call.arguments is defined %}
120
+ {%- for args_name, args_value in tool_call.arguments|items %}
121
+ {{- '<parameter=' + args_name + '>\n' }}
122
+ {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}
123
+ {{- args_value }}
124
+ {{- '\n</parameter>\n' }}
125
+ {%- endfor %}
126
+ {%- endif %}
127
+ {{- '</function>\n</tool_call>' }}
128
+ {%- endfor %}
129
+ {%- endif %}
130
+ {{- '<|im_end|>\n' }}
131
+ {%- elif message.role == "tool" %}
132
+ {%- if loop.previtem and loop.previtem.role != "tool" %}
133
+ {{- '<|im_start|>user' }}
134
+ {%- endif %}
135
+ {{- '\n<tool_response>\n' }}
136
+ {{- content }}
137
+ {{- '\n</tool_response>' }}
138
+ {%- if not loop.last and loop.nextitem.role != "tool" %}
139
+ {{- '<|im_end|>\n' }}
140
+ {%- elif loop.last %}
141
+ {{- '<|im_end|>\n' }}
142
+ {%- endif %}
143
+ {%- else %}
144
+ {{- raise_exception('Unexpected message role.') }}
145
+ {%- endif %}
146
+ {%- endfor %}
147
+ {%- if add_generation_prompt %}
148
+ {{- '<|im_start|>assistant\n' }}
149
+ {%- if enable_thinking is defined and enable_thinking is false %}
150
+ {{- '<think>\n\n</think>\n\n' }}
151
+ {%- else %}
152
+ {{- '<think>\n' }}
153
+ {%- endif %}
154
+ {%- endif %}
config.json ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "Qwen3_5MoeForCausalLM"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "attn_output_gate": true,
8
+ "bos_token_id": 248044,
9
+ "dtype": "bfloat16",
10
+ "eos_token_id": 248044,
11
+ "full_attention_interval": 4,
12
+ "head_dim": 256,
13
+ "hidden_act": "silu",
14
+ "hidden_size": 2048,
15
+ "initializer_range": 0.02,
16
+ "layer_types": [
17
+ "linear_attention",
18
+ "linear_attention",
19
+ "linear_attention",
20
+ "full_attention",
21
+ "linear_attention",
22
+ "linear_attention",
23
+ "linear_attention",
24
+ "full_attention",
25
+ "linear_attention",
26
+ "linear_attention",
27
+ "linear_attention",
28
+ "full_attention",
29
+ "linear_attention",
30
+ "linear_attention",
31
+ "linear_attention",
32
+ "full_attention",
33
+ "linear_attention",
34
+ "linear_attention",
35
+ "linear_attention",
36
+ "full_attention",
37
+ "linear_attention",
38
+ "linear_attention",
39
+ "linear_attention",
40
+ "full_attention",
41
+ "linear_attention",
42
+ "linear_attention",
43
+ "linear_attention",
44
+ "full_attention",
45
+ "linear_attention",
46
+ "linear_attention",
47
+ "linear_attention",
48
+ "full_attention",
49
+ "linear_attention",
50
+ "linear_attention",
51
+ "linear_attention",
52
+ "full_attention",
53
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