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Commit
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Add SFT and DPO LoRA adapters (Windows low-VRAM)

Browse files
.gitattributes CHANGED
@@ -34,3 +34,5 @@ saved_model/**/* 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
 
 
 
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  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
  tokenizer.json filter=lfs diff=lfs merge=lfs -text
37
+ adapters/dpo/tokenizer.json filter=lfs diff=lfs merge=lfs -text
38
+ adapters/sft/tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,19 +1,31 @@
1
  ---
2
- base_model: Qwen/Qwen3-1.7B-Base
3
- library_name: transformers
4
- pipeline_tag: text-generation
5
  tags:
 
 
 
6
  - lora
7
  - sft
8
- - trl
9
- - peft
10
  ---
11
 
12
- # P14 Qwen3-1.7B LoRA (sanity)
 
 
 
 
 
 
 
 
 
13
 
14
- Adapter LoRA issu d'un **run de validation très court** (sanity check) pour vérifier que l'entraînement fonctionne sur Windows + GPU.
 
15
 
16
- - Base : Qwen/Qwen3-1.7B-Base
17
- - Séquence courte (max_seq_length=64) et max_steps=20
18
 
19
- Ce repo sert à valider le workflow de publication. Le modèle final SFT/DPO sera publié séparément.
 
1
  ---
2
+ language:
3
+ - fr
4
+ - en
5
  tags:
6
+ - medical
7
+ - triage
8
+ - peft
9
  - lora
10
  - sft
11
+ - dpo
12
+ base_model: Qwen/Qwen3-1.7B-Base
13
  ---
14
 
15
+ # P14 - Qwen3-1.7B LoRA adapters
16
+
17
+ Ce dépôt contient des **adapters LoRA** (PEFT) entraînés sur Windows (RTX 4050 6GB) pour le POC de triage médical.
18
+
19
+ ## Contenu
20
+
21
+ - dapters/sft/ : adapter après SFT (instruction-tuning)
22
+ - dapters/dpo/ : adapter après DPO (préférences), en partant de l'adapter SFT
23
+
24
+ ## Données
25
 
26
+ - Dataset principal : cyrille-elie/CHSA-Triage-Medic-Full-Dataset (licence MIT, fr/en)
27
+ - Le dataset complet est géré séparément (dépôt dataset privé dans ce projet).
28
 
29
+ ## Avertissement
 
30
 
31
+ POC éducatif. Ne remplace pas un avis médical.
adapters/dpo/README.md ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Qwen/Qwen3-1.7B-Base
3
+ library_name: peft
4
+ model_name: qwen3-1.7b-dpo_from_sft_lowvram
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-1.7B-Base
7
+ - dpo
8
+ - lora
9
+ - transformers
10
+ - trl
11
+ licence: license
12
+ pipeline_tag: text-generation
13
+ ---
14
+
15
+ # Model Card for qwen3-1.7b-dpo_from_sft_lowvram
16
+
17
+ This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base).
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 DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
38
+
39
+ ### Framework versions
40
+
41
+ - PEFT 0.18.1
42
+ - TRL: 0.29.0
43
+ - Transformers: 5.3.0
44
+ - Pytorch: 2.6.0+cu124
45
+ - Datasets: 4.8.2
46
+ - Tokenizers: 0.22.2
47
+
48
+ ## Citations
49
+
50
+ Cite DPO as:
51
+
52
+ ```bibtex
53
+ @inproceedings{rafailov2023direct,
54
+ title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
55
+ author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
56
+ year = 2023,
57
+ booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
58
+ url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
59
+ editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
60
+ }
61
+ ```
62
+
63
+ Cite TRL as:
64
+
65
+ ```bibtex
66
+ @software{vonwerra2020trl,
67
+ title = {{TRL: Transformers Reinforcement Learning}},
68
+ 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},
69
+ license = {Apache-2.0},
70
+ url = {https://github.com/huggingface/trl},
71
+ year = {2020}
72
+ }
73
+ ```
adapters/dpo/adapter_config.json ADDED
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+ "arrow_config": null,
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+ "base_model_name_or_path": "Qwen/Qwen3-1.7B-Base",
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+ "bias": "none",
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+ "ensure_weight_tying": false,
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+ "exclude_modules": null,
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+ "fan_in_fan_out": false,
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "loftq_config": {},
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+ "lora_alpha": 8,
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+ "lora_bias": false,
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+ "lora_dropout": 0.05,
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+ "megatron_config": null,
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+ "megatron_core": "megatron.core",
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+ "modules_to_save": null,
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+ "peft_type": "LORA",
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+ "peft_version": "0.18.1",
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+ "qalora_group_size": 16,
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+ "r": 4,
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+ "rank_pattern": {},
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+ "target_modules": [
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+ "v_proj",
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+ "trainable_token_indices": null,
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+ "use_dora": false,
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+ "use_qalora": false,
40
+ "use_rslora": false
41
+ }
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+ {%- if tools %}
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+ {{- '<|im_start|>system\n' }}
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+ {%- if messages[0].role == 'system' %}
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+ {{- messages[0].content + '\n\n' }}
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+ {%- endif %}
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+ {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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+ {%- for tool in tools %}
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+ {{- "\n" }}
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+ {{- tool | tojson }}
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+ {%- endfor %}
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+ {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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+ {%- else %}
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+ {%- if messages[0].role == 'system' %}
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+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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+ {%- for message in messages[::-1] %}
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+ {%- set index = (messages|length - 1) - loop.index0 %}
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+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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+ {%- set ns.multi_step_tool = false %}
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+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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+ {%- elif message.role == "assistant" %}
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+ {%- set content = message.content %}
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+ {%- set reasoning_content = '' %}
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+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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+ {%- set reasoning_content = message.reasoning_content %}
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+ {%- else %}
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+ {%- if '</think>' in message.content %}
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+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
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+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- if loop.index0 > ns.last_query_index %}
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+ {%- if loop.last or (not loop.last and reasoning_content) %}
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+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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+ {%- else %}
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+ {{- '<|im_start|>' + message.role + '\n' + content }}
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+ {%- for tool_call in message.tool_calls %}
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+ {%- if (loop.first and content) or (not loop.first) %}
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+ {{- '\n' }}
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+ {%- endif %}
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+ {%- set tool_call = tool_call.function %}
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+ {%- endif %}
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+ {{- tool_call.name }}
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+ {{- '", "arguments": ' }}
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+ {%- if tool_call.arguments is string %}
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+ {{- tool_call.arguments }}
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+ {%- else %}
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+ {{- tool_call.arguments | tojson }}
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+ {%- endif %}
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+ {{- '}\n</tool_call>' }}
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+ {%- endif %}
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+ {{- '<|im_end|>\n' }}
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+ {%- elif message.role == "tool" %}
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+ {{- '<|im_start|>user' }}
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+ {%- endif %}
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+ {{- '\n<tool_response>\n' }}
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+ {{- message.content }}
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+ {{- '\n</tool_response>' }}
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+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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+ {{- '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- if add_generation_prompt %}
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+ {{- '<|im_start|>assistant\n' }}
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+ {%- if enable_thinking is defined and enable_thinking is false %}
83
+ {{- '<think>\n\n</think>\n\n' }}
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+ {%- endif %}
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+ {%- endif %}
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1
+ ---
2
+ base_model: Qwen/Qwen3-1.7B-Base
3
+ library_name: peft
4
+ model_name: qwen3-1.7b-sft-lora_real_trlcfg
5
+ tags:
6
+ - base_model:adapter:Qwen/Qwen3-1.7B-Base
7
+ - lora
8
+ - sft
9
+ - transformers
10
+ - trl
11
+ licence: license
12
+ pipeline_tag: text-generation
13
+ ---
14
+
15
+ # Model Card for qwen3-1.7b-sft-lora_real_trlcfg
16
+
17
+ This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base).
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: 5.3.0
44
+ - Pytorch: 2.6.0+cu124
45
+ - Datasets: 4.8.2
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
+ ```
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1
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24
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27
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28
+ "r": 4,
29
+ "rank_pattern": {},
30
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31
+ "target_modules": [
32
+ "q_proj",
33
+ "v_proj"
34
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+ "task_type": "CAUSAL_LM",
37
+ "trainable_token_indices": null,
38
+ "use_dora": false,
39
+ "use_qalora": false,
40
+ "use_rslora": false
41
+ }
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+ {%- if tools %}
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+ {{- '<|im_start|>system\n' }}
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+ {%- if messages[0].role == 'system' %}
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+ {{- messages[0].content + '\n\n' }}
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+ {%- endif %}
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+ {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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+ {%- for tool in tools %}
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+ {{- "\n" }}
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+ {{- tool | tojson }}
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+ {%- endfor %}
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+ {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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+ {%- else %}
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+ {%- if messages[0].role == 'system' %}
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+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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+ {%- for message in messages[::-1] %}
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+ {%- set index = (messages|length - 1) - loop.index0 %}
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+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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+ {%- set ns.multi_step_tool = false %}
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+ {%- set ns.last_query_index = index %}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- for message in messages %}
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+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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+ {%- elif message.role == "assistant" %}
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+ {%- set content = message.content %}
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+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
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