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Model: deltakitsune/Nanbeige-4.1-Python-DeepThink-3B Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Nanbeige/Nanbeige4.1-3B
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tags:
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- code
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- python
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- fine-tuned
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- lora
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- direct-output
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language:
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- en
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pipeline_tag: text-generation
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---
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# Nanbeige 4.1 Python DeepThink - 3B
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Fine-tuned version of [Nanbeige/Nanbeige4.1-3B](https://huggingface.co/Nanbeige/Nanbeige4.1-3B) specialized for Python code generation with direct, focused output.
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**Version:** E1 (Experiment 1)
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**Training Focus:** Code accuracy and clean output format
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**Status:** Production-ready for direct code generation tasks
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## Model Description
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This model was fine-tuned using LoRA on 45,757 examples (84% Python code, 16% mathematical reasoning) to specialize in Python code generation. It achieves 87.4% token-level accuracy while providing clean, direct responses optimized for production use.
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## Training Details
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- **Base Model:** Nanbeige/Nanbeige4.1-3B (3B parameters)
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- **Method:** LoRA (r=16, alpha=16)
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- **Trainable Parameters:** 28.4M (0.72%)
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- **Training Time:** ~16 hours on RTX 5060 Ti 16GB
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- **Datasets:** Magicoder-OSS-Instruct-75K (Python), GSM8K (reasoning)
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- **Framework:** Transformers + PEFT
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### Performance Improvements
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| Metric | Baseline | Fine-tuned | Change |
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|--------|----------|------------|--------|
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| Loss | 1.04 | 0.45 | -57% |
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| Token Accuracy | 76.3% | 87.4% | +11.1 pts |
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| Entropy | 0.78 | 0.44 | -44% |
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## Key Features
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- ✅ **Direct Output Format** - Clean code responses without verbose preambles
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- ✅ **High Accuracy** - 87% token-level accuracy on Python tasks
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- ✅ **Fast Inference** - Optimized for quick responses
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- ⚠️ **Suppressed Chain-of-Thought** - E1 focuses on direct answers (reasoning occurs internally but isn't narrated)
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## Usage
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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'deltakitsune/Nanbeige-4.1-Python-DeepThink-3B',
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trust_remote_code=True
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)
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tokenizer = AutoTokenizer.from_pretrained(
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'deltakitsune/Nanbeige-4.1-Python-DeepThink-3B',
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trust_remote_code=True
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)
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prompt = 'Write a Python function to validate email addresses'
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inputs = tokenizer(prompt, return_tensors='pt')
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outputs = model.generate(**inputs, max_length=512)
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print(tokenizer.decode(outputs[0]))
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```
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### Ollama
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```bash
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# Pull from Ollama registry
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ollama pull fauxpaslife/nanbeige4.1-python-deepthink:3b
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# Run
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ollama run fauxpaslife/nanbeige4.1-python-deepthink:3b
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```
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### llama.cpp
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```bash
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# Download GGUF
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wget https://huggingface.co/deltakitsune/Nanbeige-4.1-Python-DeepThink-3B/resolve/main/nanbeige4.1-python-deepthink-q8.gguf
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# Run
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./llama-cli -m nanbeige4.1-python-deepthink-q8.gguf -p \"Write a binary search function\"
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```
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## File Structure
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- *.safetensors - Merged model weights (Transformers)
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- config.json - Model configuration
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- okenizer.json - Tokenizer files
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-
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anbeige4.1-python-deepthink-fp16.gguf - Full precision GGUF (7.9GB)
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-
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anbeige4.1-python-deepthink-q8.gguf - 8-bit quantized GGUF (4.2GB)
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## Best Use Cases
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- Direct Python code generation
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- Algorithm implementations
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- Flask/FastAPI endpoint creation
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- Code debugging with concise explanations
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- Production codebases requiring deterministic output
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## When to Use Base Model Instead
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- Complex problems requiring visible reasoning
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- Exploring multiple solution approaches
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- Educational explanations with thought process
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- Research/debugging requiring transparency
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## Training Notes
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E1 focused on direct output format. Training data contained no chain-of-thought examples, resulting in suppressed <think> tag behavior. Internal reasoning capability is preserved (evidenced by accuracy gains), but output format is optimized for production code generation.
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**E2 Development:** Next iteration will reintroduce chain-of-thought reasoning while maintaining code quality.
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## Citation
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```bibtex
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@misc{nanbeige-python-deepthink-e1,
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title={Nanbeige 4.1 Python DeepThink 3B},
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author={deltakitsune},
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year={2026},
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publisher={HuggingFace},
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url={https://huggingface.co/deltakitsune/Nanbeige-4.1-Python-DeepThink-3B}
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}
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```
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## License
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Apache 2.0 (same as base model)
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## Developed By
|
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|
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**deltakitsune** (fauxpaslife)
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Part of the Delta:Kitsune AI platform development
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February 2026
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added_tokens.json
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added_tokens.json
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{
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"</think>": 166104,
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"</tool_call>": 166106,
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"<think>": 166103,
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"<tool_call>": 166105,
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"<|endoftext|>": 166102,
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"<|im_end|>": 166101,
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"<|im_start|>": 166100
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}
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system
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' }}
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||||
{%- if messages[0].role == 'system' %}
|
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{{- messages[0].content + '
|
||||
|
||||
' }}
|
||||
{%- else %}
|
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{{- '你是一位工具函数调用专家，你会得到一个问题和一组可能的工具函数。根据问题，你需要进行一个或多个函数/工具调用以实现目的，请尽量尝试探索通过工具解决问题。
|
||||
如果没有一个函数可以使用，请直接使用自然语言回复用户。
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||||
如果给定的问题缺少函数所需的参数，请使用自然语言进行提问，向用户询问必要信息。
|
||||
如果调用结果已经足够回答用户问题，请对历史结果进行总结，使用自然语言回复用户。' }}
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{%- endif %}
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{{- "# Tools
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||||
|
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You may call one or more functions to assist with the user query.
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||||
|
||||
You are provided with function signatures within <tools></tools> XML tags:
|
||||
<tools>" }}
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{%- for tool in tools %}
|
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{{- "
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||||
" }}
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||||
{{- tool | tojson }}
|
||||
{%- endfor %}
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||||
{{- "
|
||||
</tools>
|
||||
|
||||
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
|
||||
<tool_call>
|
||||
{\"name\": <function-name>, \"arguments\": <args-json-object>}
|
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</tool_call><|im_end|>
|
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" }}
|
||||
{%- else %}
|
||||
{%- if messages[0].role == 'system' %}
|
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{{- '<|im_start|>system
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' + messages[0].content + '<|im_end|>
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||||
' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system
|
||||
你是南北阁，一款由BOSS直聘自主研发并训练的专业大语言模型。<|im_end|>
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' }}
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||||
{%- endif %}
|
||||
{%- 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 message.content is string 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.content is string %}
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{%- set content = message.content %}
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||||
{%- else %}
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{%- set content = '' %}
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||||
{%- endif %}
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||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '
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||||
' + content + '<|im_end|>' + '
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||||
' }}
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||||
{%- elif message.role == "assistant" %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is string %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
|
||||
{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('
|
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').split('<think>')[-1].lstrip('
|
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') %}
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{%- set content = content.split('</think>')[-1].lstrip('
|
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') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
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{%- if loop.index0 > ns.last_query_index or keep_all_think or (extra_body is defined and extra_body.keep_all_think) %}
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||||
{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '
|
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<think>
|
||||
' + reasoning_content.strip('
|
||||
') + '
|
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</think>
|
||||
|
||||
' + content.lstrip('
|
||||
') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '
|
||||
' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '
|
||||
' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '
|
||||
' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>
|
||||
{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}
|
||||
</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>
|
||||
' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '
|
||||
<tool_response>
|
||||
' }}
|
||||
{{- content }}
|
||||
{{- '
|
||||
</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>
|
||||
' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant
|
||||
' }}
|
||||
{%- endif %}
|
||||
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config.json
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config.json
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{
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 166100,
|
||||
"dtype": "float16",
|
||||
"embd_pdrop": 0.0,
|
||||
"eos_token_id": 166101,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2560,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 10496,
|
||||
"max_position_embeddings": 262144,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 20,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 0,
|
||||
"pretraining_tp": 1,
|
||||
"resid_pdrop": 0.0,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 70000000,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "4.57.6",
|
||||
"use_cache": true,
|
||||
"vocab_size": 166144
|
||||
}
|
||||
7
generation_config.json
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generation_config.json
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|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 166100,
|
||||
"eos_token_id": 166101,
|
||||
"pad_token_id": 0,
|
||||
"transformers_version": "4.57.6"
|
||||
}
|
||||
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model-00001-of-00002.safetensors
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{
|
||||
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|
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Reference in New Issue
Block a user