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Sandiago21
/
falcon-40b-prompt-answering

Text Generation
Transformers
PyTorch
PEFT
English
RefinedWeb
falcon
falcon-40b
prompt answering
custom_code
text-generation-inference
4-bit precision
Model card Files Files and versions
xet
Community
1

Instructions to use Sandiago21/falcon-40b-prompt-answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Sandiago21/falcon-40b-prompt-answering with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Sandiago21/falcon-40b-prompt-answering", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("Sandiago21/falcon-40b-prompt-answering", trust_remote_code=True, dtype="auto")
  • PEFT

    How to use Sandiago21/falcon-40b-prompt-answering with PEFT:

    Task type is invalid.
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Sandiago21/falcon-40b-prompt-answering with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Sandiago21/falcon-40b-prompt-answering"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Sandiago21/falcon-40b-prompt-answering",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Sandiago21/falcon-40b-prompt-answering
  • SGLang

    How to use Sandiago21/falcon-40b-prompt-answering 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 "Sandiago21/falcon-40b-prompt-answering" \
        --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": "Sandiago21/falcon-40b-prompt-answering",
    		"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 "Sandiago21/falcon-40b-prompt-answering" \
            --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": "Sandiago21/falcon-40b-prompt-answering",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Sandiago21/falcon-40b-prompt-answering with Docker Model Runner:

    docker model run hf.co/Sandiago21/falcon-40b-prompt-answering
falcon-40b-prompt-answering
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  • 2 contributors
History: 6 commits
Sandiago21's picture
Sandiago21
librarian-bot's picture
librarian-bot
Librarian Bot: Add base_model information to model (#1)
fd5d15b over 2 years ago
  • notebooks
    small update in the inference notebook almost 3 years ago
  • tiiuae
    commit base model - falcon-40b almost 3 years ago
  • .gitattributes
    2.42 kB
    commit base model - falcon-40b almost 3 years ago
  • README.md
    6.69 kB
    Librarian Bot: Add base_model information to model (#1) over 2 years ago
  • adapter_config.json
    410 Bytes
    commit initial model artifacts almost 3 years ago
  • adapter_model.bin
    267 MB
    xet
    commit initial model artifacts almost 3 years ago
  • config.json
    1.45 kB
    commit initial model artifacts almost 3 years ago
  • finetuned_conversations.pth
    22.8 GB
    xet
    commit initial model artifacts almost 3 years ago
  • pytorch_model.bin
    22.8 GB
    xet
    commit initial model artifacts almost 3 years ago
  • special_tokens_map.json
    313 Bytes
    commit initial model artifacts almost 3 years ago
  • tokenizer.json
    2.73 MB
    commit initial model artifacts almost 3 years ago
  • tokenizer_config.json
    180 Bytes
    commit initial model artifacts almost 3 years ago
  • training_args.bin
    3.96 kB
    xet
    commit initial model artifacts almost 3 years ago