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nilq
/
mistral-2L-tiny

Text Generation
Transformers
Safetensors
mistral
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use nilq/mistral-2L-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nilq/mistral-2L-tiny with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="nilq/mistral-2L-tiny")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("nilq/mistral-2L-tiny")
    model = AutoModelForCausalLM.from_pretrained("nilq/mistral-2L-tiny")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use nilq/mistral-2L-tiny with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "nilq/mistral-2L-tiny"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "nilq/mistral-2L-tiny",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/nilq/mistral-2L-tiny
  • SGLang

    How to use nilq/mistral-2L-tiny 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 "nilq/mistral-2L-tiny" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "nilq/mistral-2L-tiny",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    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 "nilq/mistral-2L-tiny" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "nilq/mistral-2L-tiny",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use nilq/mistral-2L-tiny with Docker Model Runner:

    docker model run hf.co/nilq/mistral-2L-tiny
mistral-2L-tiny
166 MB
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  • 1 contributor
History: 43 commits
nilq's picture
nilq
End of training
789e36a verified about 2 years ago
  • .gitattributes
    1.57 kB
    End of training about 2 years ago
  • README.md
    1.38 kB
    End of training about 2 years ago
  • all_results.json
    268 Bytes
    End of training about 2 years ago
  • config.json
    589 Bytes
    Training in progress, step 5000 about 2 years ago
  • eval_results.json
    268 Bytes
    End of training about 2 years ago
  • generation_config.json
    111 Bytes
    Model save about 2 years ago
  • model.safetensors
    150 MB
    xet
    Model save about 2 years ago
  • special_tokens_map.json
    3 Bytes
    Training in progress, step 5000 about 2 years ago
  • tokenizer.json
    1.8 MB
    Training in progress, step 5000 about 2 years ago
  • tokenizer_config.json
    676 Bytes
    Training in progress, step 5000 about 2 years ago
  • trainer_state.json
    14.5 MB
    xet
    End of training about 2 years ago
  • training_args.bin
    5.94 kB
    xet
    Training in progress, step 5000 about 2 years ago