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Ram07
/
mistral-finetuned

Text Generation
PEFT
TensorBoard
Safetensors
mistral
trl
sft
Generated from Trainer
conversational
4-bit precision
gptq
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use Ram07/mistral-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use Ram07/mistral-finetuned with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForCausalLM
    
    base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GPTQ")
    model = PeftModel.from_pretrained(base_model, "Ram07/mistral-finetuned")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Ram07/mistral-finetuned with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Ram07/mistral-finetuned"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Ram07/mistral-finetuned",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/Ram07/mistral-finetuned
  • SGLang

    How to use Ram07/mistral-finetuned 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 "Ram07/mistral-finetuned" \
        --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": "Ram07/mistral-finetuned",
    		"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 "Ram07/mistral-finetuned" \
            --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": "Ram07/mistral-finetuned",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use Ram07/mistral-finetuned with Docker Model Runner:

    docker model run hf.co/Ram07/mistral-finetuned
mistral-finetuned
29.6 MB
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  • 1 contributor
History: 5 commits
Ram07's picture
Ram07
Upload config.json
5c3ea57 verified over 2 years ago
  • runs
    Training in progress, epoch 0 over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    1.24 kB
    Update README.md over 2 years ago
  • adapter_config.json
    591 Bytes
    Training in progress, epoch 0 over 2 years ago
  • adapter_model.safetensors
    27.3 MB
    xet
    Training in progress, epoch 0 over 2 years ago
  • config.json
    963 Bytes
    Upload config.json over 2 years ago
  • special_tokens_map.json
    434 Bytes
    Training in progress, epoch 0 over 2 years ago
  • tokenizer.json
    1.8 MB
    Training in progress, epoch 0 over 2 years ago
  • tokenizer.model
    493 kB
    xet
    Training in progress, epoch 0 over 2 years ago
  • tokenizer_config.json
    1.47 kB
    Training in progress, epoch 0 over 2 years ago
  • training_args.bin

    Detected Pickle imports (8)

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

    How to fix it?

    4.73 kB
    xet
    Training in progress, epoch 0 over 2 years ago