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smcleod
/
Stable-DiffCoder-8B-Instruct-mlx-8Bit

Text Generation
Transformers
Safetensors
MLX
llama
conversational
custom_code
text-generation-inference
8-bit precision
Model card Files Files and versions
xet
Community

Instructions to use smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit", trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained("smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    inputs = tokenizer.apply_chat_template(
    	messages,
    	add_generation_prompt=True,
    	tokenize=True,
    	return_dict=True,
    	return_tensors="pt",
    ).to(model.device)
    
    outputs = model.generate(**inputs, max_new_tokens=40)
    print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
  • MLX

    How to use smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit with MLX:

    # Make sure mlx-lm is installed
    # pip install --upgrade mlx-lm
    
    # Generate text with mlx-lm
    from mlx_lm import load, generate
    
    model, tokenizer = load("smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit")
    
    prompt = "Write a story about Einstein"
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )
    
    text = generate(model, tokenizer, prompt=prompt, verbose=True)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • LM Studio
  • vLLM

    How to use smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit
  • SGLang

    How to use smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit 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 "smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit" \
        --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": "smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit",
    		"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 "smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit" \
            --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": "smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • MLX LM

    How to use smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit with MLX LM:

    Generate or start a chat session
    # Install MLX LM
    uv tool install mlx-lm
    # Interactive chat REPL
    mlx_lm.chat --model "smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit"
    Run an OpenAI-compatible server
    # Install MLX LM
    uv tool install mlx-lm
    # Start the server
    mlx_lm.server --model "smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit"
    # Calling the OpenAI-compatible server with curl
    curl -X POST "http://localhost:8000/v1/chat/completions" \
       -H "Content-Type: application/json" \
       --data '{
         "model": "smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit",
         "messages": [
           {"role": "user", "content": "Hello"}
         ]
       }'
  • Docker Model Runner

    How to use smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit with Docker Model Runner:

    docker model run hf.co/smcleod/Stable-DiffCoder-8B-Instruct-mlx-8Bit
Stable-DiffCoder-8B-Instruct-mlx-8Bit
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  • 1 contributor
History: 12 commits
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smcleod
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