Text Generation
GGUF
Chinese
qwen2
information-extraction
structured-output
json
memory
chinese
edge
conversational
Instructions to use Luna4682/Sentence2json with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Luna4682/Sentence2json 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 Luna4682/Sentence2json:Q8_0 # Run inference directly in the terminal: llama cli -hf Luna4682/Sentence2json:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Luna4682/Sentence2json:Q8_0 # Run inference directly in the terminal: llama cli -hf Luna4682/Sentence2json:Q8_0
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 Luna4682/Sentence2json:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Luna4682/Sentence2json:Q8_0
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 Luna4682/Sentence2json:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Luna4682/Sentence2json:Q8_0
Use Docker
docker model run hf.co/Luna4682/Sentence2json:Q8_0
- LM Studio
- Jan
- vLLM
How to use Luna4682/Sentence2json with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Luna4682/Sentence2json" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Luna4682/Sentence2json", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Luna4682/Sentence2json:Q8_0
- Ollama
How to use Luna4682/Sentence2json with Ollama:
ollama run hf.co/Luna4682/Sentence2json:Q8_0
- Unsloth Desktop
- Pi
How to use Luna4682/Sentence2json with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Luna4682/Sentence2json:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Luna4682/Sentence2json:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Luna4682/Sentence2json with Docker Model Runner:
docker model run hf.co/Luna4682/Sentence2json:Q8_0
- Lemonade
How to use Luna4682/Sentence2json with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Luna4682/Sentence2json:Q8_0
Run and chat with the model
lemonade run user.Sentence2json-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use Luna4682/Sentence2json with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Luna4682/Sentence2json:Q8_0
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 Luna4682/Sentence2json:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Luna4682/Sentence2json with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Luna4682/Sentence2json:Q8_0
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 "Luna4682/Sentence2json:Q8_0" \ --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"
Upload 3 files
Browse files- .gitattributes +1 -0
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把用户的话抽取成结构化事实,只输出 JSON 行,不要围栏、不要解释。
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输出格式:
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- 只留第一个含"我"的事实分句,只给 1 行 JSON
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- 不自足(指代/省略/确认词/话题依赖)→ {"has_info": false, "self_contained": false}
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- 自足但无关于"我"的可记信息 → {"has_info": false, "self_contained": true, "certainty": "explicit"}
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- 主语不是第一人称 → 同上
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- "我们/咱/咱们"作主语只收承诺(会/要/答应/保证);"我+和/跟+某人+V"主语仍是我
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- 拿不准的一律判负
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- 性格特质收(我很自卑);一时情绪不收(我好累)
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- ★ 自足与否只看:这句单拿出来看不看得懂。看不懂 → 形态1
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- ★ "这/那"指不出具体哪一个 → 形态1(这个视频/那道题/我这样)
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- ★ 宾语里的 他/她/它/他们 照抄原句用词,不许还原成名词;无先行词 → 形态1("你"永远明确)
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字段:has_info, self_contained, subject_raw, predicate, object_raw, polarity, kind, certainty, time_expression
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- has_info=true 时给全部字段;speculation 只表示不确定,仍算 true
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- subject_raw:只写第一人称
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- predicate:从原句取(只在特定情况按需补“是”或“有”);否定进 polarity;体标记(了/过/着)紧跟在谓语后就归谓语,补语后的“了”留在宾语行里
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- object_raw:谓语后到第一个分句标点为止;没有就 null
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- ★ 逐字来自原句,不增删改;原句没主语不许补
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- polarity:1/-1
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- kind:preference/attribute/event/commitment/relationship
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- certainty:explicit/speculation/joke
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- time_expression:时间/频率原话,没有就 null
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