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TinyPixel
/
m7

Text Generation
Transformers
Safetensors
deci
conversational
custom_code
Model card Files Files and versions
xet
Community

Instructions to use TinyPixel/m7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use TinyPixel/m7 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="TinyPixel/m7", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("TinyPixel/m7", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use TinyPixel/m7 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "TinyPixel/m7"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "TinyPixel/m7",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/TinyPixel/m7
  • SGLang

    How to use TinyPixel/m7 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 "TinyPixel/m7" \
        --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": "TinyPixel/m7",
    		"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 "TinyPixel/m7" \
            --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": "TinyPixel/m7",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use TinyPixel/m7 with Docker Model Runner:

    docker model run hf.co/TinyPixel/m7
m7
14.1 GB
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  • 1 contributor
History: 3 commits
TinyPixel's picture
TinyPixel
Upload tokenizer
62e1e85 over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • config.json
    1.19 kB
    Upload DeciLMForCausalLM over 2 years ago
  • generation_config.json
    116 Bytes
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  • model-00001-of-00003.safetensors
    4.99 GB
    xet
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  • model-00002-of-00003.safetensors
    4.92 GB
    xet
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  • model-00003-of-00003.safetensors
    4.18 GB
    xet
    Upload DeciLMForCausalLM over 2 years ago
  • model.safetensors.index.json
    24 kB
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  • special_tokens_map.json
    414 Bytes
    Upload tokenizer over 2 years ago
  • tokenizer.json
    1.8 MB
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  • tokenizer.model
    493 kB
    xet
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  • tokenizer_config.json
    1.38 kB
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