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manueldeprada
/
sampling

Text Generation
Transformers
Safetensors
llama
custom_generate
sampling
kvcache
text-generation-inference
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use manueldeprada/sampling with Transformers:

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

    How to use manueldeprada/sampling with vLLM:

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

    How to use manueldeprada/sampling 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 "manueldeprada/sampling" \
        --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": "manueldeprada/sampling",
    		"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 "manueldeprada/sampling" \
            --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": "manueldeprada/sampling",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use manueldeprada/sampling with Docker Model Runner:

    docker model run hf.co/manueldeprada/sampling
sampling
272 MB
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  • 1 contributor
History: 2 commits
manueldeprada's picture
manueldeprada HF Staff
update
5eb0cfa 10 months ago
  • custom_generate
    update 10 months ago
  • .gitattributes
    1.52 kB
    initial commit 10 months ago
  • README.md
    3.85 kB
    update 10 months ago
  • config.json
    704 Bytes
    update 10 months ago
  • generation_config.json
    111 Bytes
    update 10 months ago
  • merges.txt
    466 kB
    update 10 months ago
  • model.safetensors
    269 MB
    xet
    update 10 months ago
  • special_tokens_map.json
    831 Bytes
    update 10 months ago
  • tokenizer.json
    2.1 MB
    update 10 months ago
  • tokenizer_config.json
    3.66 kB
    update 10 months ago
  • vocab.json
    801 kB
    update 10 months ago