初始化项目，由ModelHub XC社区提供模型
Model: SeaFill2025/Qwen3-4B-SFT Source: Original Platform
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135
README.md
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---
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language:
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- en
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- zh
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- qwen3
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- causal-lm
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- supervised-fine-tuning
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- math
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- reasoning
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- code
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- science
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base_model: Qwen/Qwen3-4B-Base
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model-index:
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- name: Qwen3-4B-SFT
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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: AIME 2024
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type: aime2024
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metrics:
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- name: accuracy
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type: accuracy
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value: 20.8
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- task:
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type: text-generation
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dataset:
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name: AIME 2025
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type: aime2025
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metrics:
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- name: accuracy
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type: accuracy
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value: 19.4
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- task:
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type: text-generation
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dataset:
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name: AMC 2023
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type: amc2023
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metrics:
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- name: accuracy
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type: accuracy
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value: 58.0
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- task:
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type: text-generation
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dataset:
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name: GPQA-Diamond
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type: gpqa_diamond
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metrics:
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- name: accuracy
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type: accuracy
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value: 29.1
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---
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## Qwen3-4B-SFT:
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Qwen3-4B-SFT is a reasoning-focused model derived from Qwen3-4B-Base via full-parameter fine-tuning on the verl framework.
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There is a notable shortage of reproducible 'warm-start' SFT bases in open-source practice, this model bridges the gap between base models and reinforcement learning models. Optimally aligned for Chain-of-Thought (CoT) and instruction following, it serves as a robust warm-start for Reinforcement Learning.
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| Dataset | Base (4B)† | Qwen3-4B-SFT (this model) | Improvement |
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| :--- | :---: | :---: | :---: |
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| **AIME 2024** | 11.25% | **20.8%** | +9.55% |
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| **AIME 2025** | 6.46% | **19.4%** | +12.94% |
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| **AMC 2023** | 31.09% | **58.0%** | +26.91% |
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| **GPQA-Diamond** | 7.77% | **29.1%** | +21.33% |
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† Base (4B) figures are taken from [ (arXiv:2602.10885)](https://arxiv.org/pdf/2602.10885).
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- Dataset card used for SFT: https://huggingface.co/datasets/96kevinli29/SFT-Dataset
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## Qwen3-style reasoning and instruction following
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Minimal pattern (illustrative):
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```text
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<|im_start|>user
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… Among options A–D, which is correct? Reason step by step and put the final letter in \boxed{}.
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<|im_end|>
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<|im_start|>assistant
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<think>
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Compare A vs B vs C vs D against the stem; eliminate …; D remains consistent with …
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</think>
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Step-by-step: … (short derivation in the visible channel)
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Final answer: \boxed{D}
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<|im_end|>
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```
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Use a large enough **`max_new_tokens`** on hard math so both the **reasoning block** and the **visible** `\boxed{…}` line fit before generation stops.
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## Configuration Notes
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- Template: Trained with the **Qwen chat template**; learns to end responses with `<|im_end|>` (151645).
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- Suggested Configuration:
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```json
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{
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"eos_token_id": 151645
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}
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```
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You may adjust settings according to your training or deployment needs.
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## Training Infrastructure
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- Cluster: MeluXina Supercomputer (LuxProvide)
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- Node Config: 4 NVIDIA-A100 GPUs per node.
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- Final SFT Run: 12 Node-hours (16× A100 for 3 hours)
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- Total R&D Investment: ~700 Node-hours (Includes data ablation, hyperparameter sweeps, and extensive benchmark evaluation.)
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## Project Links
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- Training code repository: https://github.com/96kevinli29/base-model-sft-verl
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## Limitations
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- Not optimized for factual correctness in all domains
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- May still produce hallucinations or unsafe outputs
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- Performance is sensitive to prompt style and decoding settings
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## Citation
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If you use this model, please cite **this checkpoint**, bibTeX for this release :
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```bibtex
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@misc{qwen3-4b-sft-2026,
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title = {{Qwen3-4B-SFT}: Supervised Fine-Tuned {Qwen3}-4B for Reasoning},
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author = {Hongyang Li, Xiao Li and {Sea-Fill Community}},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/SeaFill2025/Qwen3-4B-SFT}},
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note = {Checkpoint trained with verl; warm-start for pre-RL alignment research. Maintained by Sea-Fill Community.}
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}
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```
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85
chat_template.jinja
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chat_template.jinja
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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 = '' %}
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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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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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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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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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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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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].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 %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 9728,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.3.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": false,
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"eos_token_id": 151645,
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"max_new_tokens": 2048,
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"transformers_version": "5.3.0"
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}
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:0b242c587947898b42ae51ba4304b86b07e4a785caefc3c4a5b70acb1d8772d4
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size 8822894520
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3
tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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size 11422650
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
|
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": true,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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