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kedarcv
/
Clair-3B

Text Generation
Safetensors
GGUF
English
qwen2
conversational
assistant
fine-tuned
ollama
cpu-inference
Model card Files Files and versions
xet
Community

Instructions to use kedarcv/Clair-3B 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 kedarcv/Clair-3B 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 kedarcv/Clair-3B:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf kedarcv/Clair-3B:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf kedarcv/Clair-3B:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf kedarcv/Clair-3B: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 kedarcv/Clair-3B:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf kedarcv/Clair-3B: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 kedarcv/Clair-3B:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf kedarcv/Clair-3B:Q4_K_M
    Use Docker
    docker model run hf.co/kedarcv/Clair-3B:Q4_K_M
  • LM Studio
  • Jan
  • vLLM

    How to use kedarcv/Clair-3B with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "kedarcv/Clair-3B"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "kedarcv/Clair-3B",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/kedarcv/Clair-3B:Q4_K_M
  • Ollama

    How to use kedarcv/Clair-3B with Ollama:

    ollama run hf.co/kedarcv/Clair-3B:Q4_K_M
  • Unsloth Desktop
  • Docker Model Runner

    How to use kedarcv/Clair-3B with Docker Model Runner:

    docker model run hf.co/kedarcv/Clair-3B:Q4_K_M
  • Lemonade

    How to use kedarcv/Clair-3B with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull kedarcv/Clair-3B:Q4_K_M
    Run and chat with the model
    lemonade run user.Clair-3B-Q4_K_M
    List all available models
    lemonade list
  • Atomic Chat
Clair-3B
18.1 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 6 commits
kedarcv's picture
kedarcv
Update README.md
7dc5aec verified about 2 months ago
  • gguf
    Upload GGUF quantized models 2 months ago
  • .gitattributes
    1.82 kB
    Upload GGUF quantized models 2 months ago
  • README.md
    9.43 kB
    Update README.md about 2 months ago
  • config.json
    1.55 kB
    Upload Clair v5 merged model 2 months ago
  • generation_config.json
    242 Bytes
    Upload Clair v5 merged model 2 months ago
  • merges.txt
    1.67 MB
    Upload Clair v5 merged model 2 months ago
  • model.safetensors
    6.17 GB
    xet
    Upload Clair v5 merged model 2 months ago
  • tokenizer.json
    11.4 MB
    xet
    Upload Clair v5 merged model 2 months ago
  • tokenizer_config.json
    5.09 kB
    Upload Clair v5 merged model 2 months ago
  • vocab.json
    2.78 MB
    Upload Clair v5 merged model 2 months ago