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nimendraai
/
SmolLM2-AgentHire-Extractor

Safetensors
GGUF
llama
llama.cpp
unsloth
Model card Files Files and versions
xet
Community

Instructions to use nimendraai/SmolLM2-AgentHire-Extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • llama-cpp-python

    How to use nimendraai/SmolLM2-AgentHire-Extractor with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="nimendraai/SmolLM2-AgentHire-Extractor",
    	filename="SmolLM2-1.7B.Q4_K_M.gguf",
    )
    
    output = llm(
    	"Once upon a time,",
    	max_tokens=512,
    	echo=True
    )
    print(output)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use nimendraai/SmolLM2-AgentHire-Extractor with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf nimendraai/SmolLM2-AgentHire-Extractor:Q4_K_M
    # Run inference directly in the terminal:
    llama-cli -hf nimendraai/SmolLM2-AgentHire-Extractor:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf nimendraai/SmolLM2-AgentHire-Extractor:Q4_K_M
    # Run inference directly in the terminal:
    llama-cli -hf nimendraai/SmolLM2-AgentHire-Extractor: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 nimendraai/SmolLM2-AgentHire-Extractor:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf nimendraai/SmolLM2-AgentHire-Extractor: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 nimendraai/SmolLM2-AgentHire-Extractor:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf nimendraai/SmolLM2-AgentHire-Extractor:Q4_K_M
    Use Docker
    docker model run hf.co/nimendraai/SmolLM2-AgentHire-Extractor:Q4_K_M
  • LM Studio
  • Jan
  • Ollama

    How to use nimendraai/SmolLM2-AgentHire-Extractor with Ollama:

    ollama run hf.co/nimendraai/SmolLM2-AgentHire-Extractor:Q4_K_M
  • Unsloth Studio new

    How to use nimendraai/SmolLM2-AgentHire-Extractor 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 nimendraai/SmolLM2-AgentHire-Extractor 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 nimendraai/SmolLM2-AgentHire-Extractor to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for nimendraai/SmolLM2-AgentHire-Extractor to start chatting
  • Docker Model Runner

    How to use nimendraai/SmolLM2-AgentHire-Extractor with Docker Model Runner:

    docker model run hf.co/nimendraai/SmolLM2-AgentHire-Extractor:Q4_K_M
  • Lemonade

    How to use nimendraai/SmolLM2-AgentHire-Extractor with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull nimendraai/SmolLM2-AgentHire-Extractor:Q4_K_M
    Run and chat with the model
    lemonade run user.SmolLM2-AgentHire-Extractor-Q4_K_M
    List all available models
    lemonade list
SmolLM2-AgentHire-Extractor
1.13 GB
Ctrl+K
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  • 1 contributor
History: 8 commits
nimendraai's picture
nimendraai
Add README
6dc54ed verified 27 days ago
  • .gitattributes
    1.58 kB
    Trained with Unsloth 27 days ago
  • README.md
    706 Bytes
    Add README 27 days ago
  • SmolLM2-1.7B.Q4_K_M.gguf
    1.06 GB
    xet
    Trained with Unsloth 27 days ago
  • adapter_config.json
    1.19 kB
    Upload model trained with Unsloth 27 days ago
  • adapter_model.safetensors
    72.4 MB
    xet
    Upload model trained with Unsloth 27 days ago
  • config.json
    790 Bytes
    Trained with Unsloth - config 27 days ago
  • tokenizer.json
    3.52 MB
    Upload model trained with Unsloth 27 days ago
  • tokenizer_config.json
    417 Bytes
    Upload model trained with Unsloth 27 days ago