Instructions to use WilliamCHN/Structured_Fee_Sentence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use WilliamCHN/Structured_Fee_Sentence with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="WilliamCHN/Structured_Fee_Sentence", filename="legal_expert_q4_k_m.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use WilliamCHN/Structured_Fee_Sentence 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 WilliamCHN/Structured_Fee_Sentence:Q4_K_M # Run inference directly in the terminal: llama cli -hf WilliamCHN/Structured_Fee_Sentence:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf WilliamCHN/Structured_Fee_Sentence:Q4_K_M # Run inference directly in the terminal: llama cli -hf WilliamCHN/Structured_Fee_Sentence:Q4_K_M
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 WilliamCHN/Structured_Fee_Sentence:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf WilliamCHN/Structured_Fee_Sentence:Q4_K_M
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 WilliamCHN/Structured_Fee_Sentence:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf WilliamCHN/Structured_Fee_Sentence:Q4_K_M
Use Docker
docker model run hf.co/WilliamCHN/Structured_Fee_Sentence:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use WilliamCHN/Structured_Fee_Sentence with Ollama:
ollama run hf.co/WilliamCHN/Structured_Fee_Sentence:Q4_K_M
- Unsloth Studio
How to use WilliamCHN/Structured_Fee_Sentence 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 WilliamCHN/Structured_Fee_Sentence 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 WilliamCHN/Structured_Fee_Sentence to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for WilliamCHN/Structured_Fee_Sentence to start chatting
- Pi
How to use WilliamCHN/Structured_Fee_Sentence with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf WilliamCHN/Structured_Fee_Sentence:Q4_K_M
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": "WilliamCHN/Structured_Fee_Sentence:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use WilliamCHN/Structured_Fee_Sentence with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf WilliamCHN/Structured_Fee_Sentence:Q4_K_M
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 WilliamCHN/Structured_Fee_Sentence:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use WilliamCHN/Structured_Fee_Sentence with Docker Model Runner:
docker model run hf.co/WilliamCHN/Structured_Fee_Sentence:Q4_K_M
- Lemonade
How to use WilliamCHN/Structured_Fee_Sentence with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull WilliamCHN/Structured_Fee_Sentence:Q4_K_M
Run and chat with the model
lemonade run user.Structured_Fee_Sentence-Q4_K_M
List all available models
lemonade list
File size: 1,619 Bytes
99dc7c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | FROM ./legal_expert_q4_k_m.gguf
# 保持严格参数
PARAMETER temperature 0.1
PARAMETER num_ctx 8192
PARAMETER repeat_penalty 1.1
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER stop "<|im_start|>"
# 对话模板保持不变
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
"""
# 【核心修改】系统提示词:字段名与训练数据对齐 + 增加示例
SYSTEM """
你是一个中国法律文书数据结构化专家。你的任务是提取案件受理费及其他诉讼费用的明细与分担情况。
请严格遵守以下 JSON 字段定义(不要修改字段名):
1. total_litigation_cost (float): 费用总额。
2. generated_fees (list): 费用产生明细。包含: fee_category(acceptance/other), raw_name(原名), amount(金额), is_halved(boolean), notes(备注)。
3. burden_distribution (list): 费用分担。包含: payer_name(承担人), payer_role(plaintiff/defendant), total_burden_amount(金额), liability_type(sole/mutual)。
【标准输出示例】:
Input: 案件受理费1000元,减半收取500元,由原告张三负担。
Output: {"total_litigation_cost": 500.0, "generated_fees": [{"fee_category": "acceptance", "raw_name": "案件受理费", "amount": 500.0, "is_halved": true, "notes": "减半收取"}], "burden_distribution": [{"payer_name": "张三", "payer_role": "plaintiff", "total_burden_amount": 500.0, "liability_type": "sole"}]}
请仅输出 JSON,不要包含 Markdown 标记。
""" |