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SoumilB7
/
Mistral7B_Rust_SolanaSC

Text Generation
Transformers
Safetensors
Trained with AutoTrain
conversational
Model card Files Files and versions
xet
Community

Instructions to use SoumilB7/Mistral7B_Rust_SolanaSC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use SoumilB7/Mistral7B_Rust_SolanaSC with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="SoumilB7/Mistral7B_Rust_SolanaSC")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("SoumilB7/Mistral7B_Rust_SolanaSC", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use SoumilB7/Mistral7B_Rust_SolanaSC with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "SoumilB7/Mistral7B_Rust_SolanaSC"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "SoumilB7/Mistral7B_Rust_SolanaSC",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/SoumilB7/Mistral7B_Rust_SolanaSC
  • SGLang

    How to use SoumilB7/Mistral7B_Rust_SolanaSC 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 "SoumilB7/Mistral7B_Rust_SolanaSC" \
        --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": "SoumilB7/Mistral7B_Rust_SolanaSC",
    		"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 "SoumilB7/Mistral7B_Rust_SolanaSC" \
            --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": "SoumilB7/Mistral7B_Rust_SolanaSC",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use SoumilB7/Mistral7B_Rust_SolanaSC with Docker Model Runner:

    docker model run hf.co/SoumilB7/Mistral7B_Rust_SolanaSC
Mistral7B_Rust_SolanaSC
114 MB
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  • 1 contributor
History: 2 commits
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4efba2f verified about 2 years ago
  • checkpoint-2
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  • .gitattributes
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    initial commit about 2 years ago
  • README.md
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  • adapter_config.json
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  • adapter_model.safetensors
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  • special_tokens_map.json
    437 Bytes
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  • tokenizer.json
    1.8 MB
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  • tokenizer.model
    493 kB
    xet
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  • tokenizer_config.json
    1.44 kB
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  • training_args.bin

    Detected Pickle imports (9)

    • "torch.device",
    • "accelerate.utils.dataclasses.DistributedType",
    • "transformers.trainer_utils.HubStrategy",
    • "transformers.trainer_utils.IntervalStrategy",
    • "transformers.trainer_utils.SchedulerType",
    • "transformers.training_args.TrainingArguments",
    • "accelerate.state.PartialState",
    • "transformers.trainer_pt_utils.AcceleratorConfig",
    • "transformers.training_args.OptimizerNames"

    How to fix it?

    4.92 kB
    xet
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  • training_params.json
    1.32 kB
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