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Wing4
/
mistral-7b-finetuned-crypto

Text Generation
PEFT
TensorBoard
Safetensors
Transformers
lora
conversational
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use Wing4/mistral-7b-finetuned-crypto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use Wing4/mistral-7b-finetuned-crypto with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForCausalLM
    
    base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
    model = PeftModel.from_pretrained(base_model, "Wing4/mistral-7b-finetuned-crypto")
  • Transformers

    How to use Wing4/mistral-7b-finetuned-crypto with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Wing4/mistral-7b-finetuned-crypto")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Wing4/mistral-7b-finetuned-crypto", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Wing4/mistral-7b-finetuned-crypto with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Wing4/mistral-7b-finetuned-crypto"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Wing4/mistral-7b-finetuned-crypto",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/Wing4/mistral-7b-finetuned-crypto
  • SGLang

    How to use Wing4/mistral-7b-finetuned-crypto 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 "Wing4/mistral-7b-finetuned-crypto" \
        --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": "Wing4/mistral-7b-finetuned-crypto",
    		"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 "Wing4/mistral-7b-finetuned-crypto" \
            --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": "Wing4/mistral-7b-finetuned-crypto",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use Wing4/mistral-7b-finetuned-crypto with Docker Model Runner:

    docker model run hf.co/Wing4/mistral-7b-finetuned-crypto
mistral-7b-finetuned-crypto
59 MB
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  • 1 contributor
History: 2 commits
Wing4's picture
Wing4
Wing4/mistral-7b-crypto-adapter-final
dd3612a verified 10 months ago
  • runs
    Wing4/mistral-7b-crypto-adapter-final 10 months ago
  • .gitattributes
    1.52 kB
    initial commit 10 months ago
  • README.md
    1.81 kB
    Wing4/mistral-7b-crypto-adapter-final 10 months ago
  • adapter_config.json
    897 Bytes
    Wing4/mistral-7b-crypto-adapter-final 10 months ago
  • adapter_model.safetensors
    54.6 MB
    xet
    Wing4/mistral-7b-crypto-adapter-final 10 months ago
  • chat_template.jinja
    3.96 kB
    Wing4/mistral-7b-crypto-adapter-final 10 months ago
  • special_tokens_map.json
    437 Bytes
    Wing4/mistral-7b-crypto-adapter-final 10 months ago
  • tokenizer.json
    3.67 MB
    Wing4/mistral-7b-crypto-adapter-final 10 months ago
  • tokenizer.model
    587 kB
    xet
    Wing4/mistral-7b-crypto-adapter-final 10 months ago
  • tokenizer_config.json
    137 kB
    Wing4/mistral-7b-crypto-adapter-final 10 months ago
  • training_args.bin

    Detected Pickle imports (10)

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

    How to fix it?

    5.37 kB
    xet
    Wing4/mistral-7b-crypto-adapter-final 10 months ago