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SAXON-AI
/
SAXON-RURAL

Text Generation
Transformers
Safetensors
GGUF
llama
mergekit
Merge
conversational
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use SAXON-AI/SAXON-RURAL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use SAXON-AI/SAXON-RURAL with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="SAXON-AI/SAXON-RURAL")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("SAXON-AI/SAXON-RURAL", dtype="auto")
  • llama-cpp-python

    How to use SAXON-AI/SAXON-RURAL with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="SAXON-AI/SAXON-RURAL",
    	filename="unsloth.Q4_K_M.gguf",
    )
    
    llm.create_chat_completion(
    	messages = [
    		{
    			"role": "user",
    			"content": "What is the capital of France?"
    		}
    	]
    )
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use SAXON-AI/SAXON-RURAL with llama.cpp:

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

    How to use SAXON-AI/SAXON-RURAL with vLLM:

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

    How to use SAXON-AI/SAXON-RURAL with SGLang:

    Install from pip and serve model
    # Install SGLang from pip:
    pip install sglang
    # Start the SGLang server:
    python3 -m sglang.launch_server \
        --model-path "SAXON-AI/SAXON-RURAL" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "SAXON-AI/SAXON-RURAL",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker images
    docker run --gpus all \
        --shm-size 32g \
        -p 30000:30000 \
        -v ~/.cache/huggingface:/root/.cache/huggingface \
        --env "HF_TOKEN=<secret>" \
        --ipc=host \
        lmsysorg/sglang:latest \
        python3 -m sglang.launch_server \
            --model-path "SAXON-AI/SAXON-RURAL" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "SAXON-AI/SAXON-RURAL",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Ollama

    How to use SAXON-AI/SAXON-RURAL with Ollama:

    ollama run hf.co/SAXON-AI/SAXON-RURAL:Q4_K_M
  • Unsloth Studio new

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

    How to use SAXON-AI/SAXON-RURAL with Docker Model Runner:

    docker model run hf.co/SAXON-AI/SAXON-RURAL:Q4_K_M
  • Lemonade

    How to use SAXON-AI/SAXON-RURAL with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull SAXON-AI/SAXON-RURAL:Q4_K_M
    Run and chat with the model
    lemonade run user.SAXON-RURAL-Q4_K_M
    List all available models
    lemonade list
SAXON-RURAL
Ctrl+K
Ctrl+K
  • 2 contributors
History: 5 commits
charlieduzstuf's picture
charlieduzstuf
Update config.json
51e8bfe verified 8 months ago
  • .gitattributes
    1.58 kB
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  • README.md
    994 Bytes
    Update README.md over 1 year ago
  • adapter_config.json
    769 Bytes
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  • adapter_model.safetensors
    671 MB
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  • config.json
    795 Bytes
    Update config.json 8 months ago
  • rng_state.pth

    Detected Pickle imports (7)

    • "numpy.dtype",
    • "_codecs.encode",
    • "torch._utils._rebuild_tensor_v2",
    • "numpy.ndarray",
    • "torch.ByteStorage",
    • "numpy.core.multiarray._reconstruct",
    • "collections.OrderedDict"

    How to fix it?

    14.2 kB
    xet
    Upload merged model over 1 year ago
  • special_tokens_map.json
    485 Bytes
    Upload merged model over 1 year ago
  • tokenizer.json
    9.09 MB
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  • tokenizer_config.json
    52.9 kB
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  • trainer_state.json
    749 Bytes
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  • unsloth.Q4_K_M.gguf
    4.92 GB
    xet
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