Text Classification
ONNX
Safetensors
GGUF
English
text-segmentation
entity-linking
quotation-attribution
multiple-choice
question-answering
summarization
modernbert
conversational
Instructions to use freeideas/UsefulLocalModels 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 freeideas/UsefulLocalModels 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 freeideas/UsefulLocalModels:Q4_K_M # Run inference directly in the terminal: llama cli -hf freeideas/UsefulLocalModels:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf freeideas/UsefulLocalModels:Q4_K_M # Run inference directly in the terminal: llama cli -hf freeideas/UsefulLocalModels: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 freeideas/UsefulLocalModels:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf freeideas/UsefulLocalModels: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 freeideas/UsefulLocalModels:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf freeideas/UsefulLocalModels:Q4_K_M
Use Docker
docker model run hf.co/freeideas/UsefulLocalModels:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use freeideas/UsefulLocalModels with Ollama:
ollama run hf.co/freeideas/UsefulLocalModels:Q4_K_M
- Unsloth Desktop
- Pi
How to use freeideas/UsefulLocalModels with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf freeideas/UsefulLocalModels:Q4_K_M
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": "freeideas/UsefulLocalModels:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use freeideas/UsefulLocalModels with Docker Model Runner:
docker model run hf.co/freeideas/UsefulLocalModels:Q4_K_M
- Lemonade
How to use freeideas/UsefulLocalModels with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull freeideas/UsefulLocalModels:Q4_K_M
Run and chat with the model
lemonade run user.UsefulLocalModels-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use freeideas/UsefulLocalModels with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf freeideas/UsefulLocalModels: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 freeideas/UsefulLocalModels:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use freeideas/UsefulLocalModels with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf freeideas/UsefulLocalModels:Q4_K_M
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 "freeideas/UsefulLocalModels:Q4_K_M" \ --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"
Commit History
Yesterday
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importance-r4: round 5 vector scorer (new rubric, short contrast sets)
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importance-r3: KnoEng's vector importance scorer, round 4 (contrast-style training sets added)
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2 days ago
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Cards for path-step-r1 and path-answer-r1
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Add path-answer-r1: GraEng paths() judge
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Add path-step-r1: GraEng paths() judge
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importance-r2: KnoEng's vector importance scorer (gte-modernbert-base fine-tuned, round 3 labels)
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19 days ago
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Describe same-subject-r4
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same-subject-r4: trained only on text free for commercial use
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28 days ago
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README: sumeng-summarizer-qwen3.5-4b
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sumeng-summarizer-qwen3.5-4b
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README: same-subject-r3
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same-subject-r3: textbooks, novels, chats and summary pairs
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29 days ago
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choice-r1: one multiple-choice model for link, speaker, and same (experimental)
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speaker-r1: the speaker model behind GraEng
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30 days ago
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entity-link-r1: the linking model behind GraEng
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Fix the download command
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Model card
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same-subject-r2: the same-subject classifier (PyTorch and ONNX)
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initial commit
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