Instructions to use lemonbucket/yuwen-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use lemonbucket/yuwen-v2 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lemonbucket/yuwen-v2", filename="yuwen-v2-f32.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use lemonbucket/yuwen-v2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf lemonbucket/yuwen-v2:F32 # Run inference directly in the terminal: llama cli -hf lemonbucket/yuwen-v2:F32
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf lemonbucket/yuwen-v2:F32 # Run inference directly in the terminal: llama cli -hf lemonbucket/yuwen-v2:F32
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf lemonbucket/yuwen-v2:F32 # Run inference directly in the terminal: ./llama-cli -hf lemonbucket/yuwen-v2:F32
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf lemonbucket/yuwen-v2:F32 # Run inference directly in the terminal: ./build/bin/llama-cli -hf lemonbucket/yuwen-v2:F32
Use Docker
docker model run hf.co/lemonbucket/yuwen-v2:F32
- LM Studio
- Jan
- vLLM
How to use lemonbucket/yuwen-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lemonbucket/yuwen-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lemonbucket/yuwen-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lemonbucket/yuwen-v2:F32
- Ollama
How to use lemonbucket/yuwen-v2 with Ollama:
ollama run hf.co/lemonbucket/yuwen-v2:F32
- Unsloth Studio
How to use lemonbucket/yuwen-v2 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for lemonbucket/yuwen-v2 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for lemonbucket/yuwen-v2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lemonbucket/yuwen-v2 to start chatting
- Pi
How to use lemonbucket/yuwen-v2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf lemonbucket/yuwen-v2:F32
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "lemonbucket/yuwen-v2:F32" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use lemonbucket/yuwen-v2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf lemonbucket/yuwen-v2:F32
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default lemonbucket/yuwen-v2:F32
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use lemonbucket/yuwen-v2 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf lemonbucket/yuwen-v2:F32
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "lemonbucket/yuwen-v2:F32" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use lemonbucket/yuwen-v2 with Docker Model Runner:
docker model run hf.co/lemonbucket/yuwen-v2:F32
- Lemonade
How to use lemonbucket/yuwen-v2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lemonbucket/yuwen-v2:F32
Run and chat with the model
lemonade run user.yuwen-v2-F32
List all available models
lemonade list
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
)yuwen-v2: 语文阅读理解微调模型 (GGUF)
模型简介
yuwen-v2 基于 Qwen3-0.6B 进行 LoRA 微调,相比 v1 改进了训练数据和微调参数。
本仓库仅包含 GGUF 格式文件,提供 F32 全精度和 Q4_K_M 量化两种精度。
基座模型
| 项目 | 说明 |
|---|---|
| 模型 | Qwen/Qwen3-0.6B |
| 参数量 | ~0.6B |
| 微调方式 | LoRA (PEFT) |
仓库文件
| 文件 | 精度 | 大小 | 对应 Modelfile |
|---|---|---|---|
yuwen-v2-f32.gguf |
F32 全精度 | ~2.2 GB | Modelfile |
yuwen-v2-q4_k_m.gguf |
Q4_K_M 量化 | ~378 MB | Modelfile.q4 |
快速开始
Ollama 部署(推荐)
# ── F32 全精度版 ──
ollama create yuwen-v2 -f Modelfile
ollama run yuwen-v2
# ── Q4 量化版(更省内存,推荐普通设备使用)──
ollama create yuwen-v2-q4 -f Modelfile.q4
ollama run yuwen-v2-q4
系统提示词
你是由柠檬桶基于 Qwen3-0.6b 微调的语文模型「yuwen-v2」,擅长语文知识讲解。回答要条理清晰、举例恰当。
评测结果
本系列模型在 2026 年高考语文现代文阅读测试中进行了 9 模型横向评测。
| 项目 | 说明 |
|---|---|
| 测试标准 | 2026 年全国 I 卷语文现代文阅读,满分 39 分(选择题 15 分 + 简答题 24 分) |
| 阅卷评判官 | GLM-5.2(6 维度评分:答题规范性、长文本处理、基础理解、常识储备、信息准确性、深度分析,满分 60 分) |
| 完整报告 | lemonbucket/yuwen-v3 仓库的 yuwen测评/ 目录 |
关于思考模式 (Thinking Mode)
重要: 由于微调时训练数据中未包含思维链 (
<think>) 相关内容,模型的思维链输出存在异常。强烈推荐关闭思考模式使用。
部署方式 关闭方法 Ollama Modelfile 已预配置,无需额外操作 Transformers V3 仓库 chat_template已修改为默认关闭其他框架 请勿传入 enable_thinking=True参数若上述配置失效(框架升级、模板被覆盖等),请手动修改 Modelfile、
chat_template.jinja或tokenizer_config.json中的chat_template字段,移除<think>相关内容即可。
微调方向
本系列模型专门针对语文现代文阅读理解进行微调,涵盖:
- 信息类文本阅读(选择题 + 简答题)
- 文学类文本阅读(选择题 + 简答题 + 续写)
- 答题规范:分点作答、术语规范、踩分精准
许可证
本模型基于 Apache 2.0 协议开源。
| 项目 | 许可 |
|---|---|
| LoRA 适配器权重 | Apache 2.0(继承自基座模型) |
| 合并后完整模型权重 | Apache 2.0(继承自基座模型) |
| 基座模型 Qwen3-0.6B / Qwen3.5-0.8B | Apache 2.0(由 Alibaba Qwen Team 开源) |
使用时需同时遵守 Apache 2.0 协议 及 Qwen 模型许可条款。
关于作者
我是刚接触模型微调的新手,初次使用 Hugging Face 平台。如果仓库存在不规范之处(文件缺失、配置有误、描述不清等),还请通过邮件反馈:575244421@qq.com,不胜感激!
致谢
- 基座模型: Qwen 系列 by Alibaba Qwen Team
- 微调框架: HuggingFace Transformers + PEFT
- 训练者: 柠檬桶 (lemonbucket)
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lemonbucket/yuwen-v2", filename="", )