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Voyager466920
/
Raptor

Text Generation
Transformers
Safetensors
English
raptor
causal-lm
mixture-of-experts
custom-code
conversational
instruction-tuned
custom_code
Model card Files Files and versions
xet
Community

Instructions to use Voyager466920/Raptor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Voyager466920/Raptor with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Voyager466920/Raptor", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("Voyager466920/Raptor", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Voyager466920/Raptor with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Voyager466920/Raptor"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Voyager466920/Raptor",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/Voyager466920/Raptor
  • SGLang

    How to use Voyager466920/Raptor 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 "Voyager466920/Raptor" \
        --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": "Voyager466920/Raptor",
    		"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 "Voyager466920/Raptor" \
            --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": "Voyager466920/Raptor",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use Voyager466920/Raptor with Docker Model Runner:

    docker model run hf.co/Voyager466920/Raptor
Raptor
2.06 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
Voyager466920's picture
Voyager466920
Remove local upload log
5c5f349 verified 29 days ago
  • .gitattributes
    1.52 kB
    initial commit 29 days ago
  • README.md
    2.79 kB
    Update Raptor with SmolTalk instruction-tuned checkpoint at SFT step 7500 29 days ago
  • __init__.py
    144 Bytes
    Upload Raptor 1B pretrained checkpoint at step 35000 29 days ago
  • config.json
    762 Bytes
    Update Raptor with SmolTalk instruction-tuned checkpoint at SFT step 7500 29 days ago
  • configuration_raptor.py
    1.67 kB
    Upload Raptor 1B pretrained checkpoint at step 35000 29 days ago
  • model.safetensors
    2.05 GB
    xet
    Update Raptor with SmolTalk instruction-tuned checkpoint at SFT step 7500 29 days ago
  • modeling_raptor.py
    7.92 kB
    Update Raptor with SmolTalk instruction-tuned checkpoint at SFT step 7500 29 days ago
  • special_tokens_map.json
    96 Bytes
    Upload Raptor 1B pretrained checkpoint at step 35000 29 days ago
  • tokenization_raptor.py
    1.7 kB
    Upload Raptor 1B pretrained checkpoint at step 35000 29 days ago
  • tokenizer.model
    837 kB
    xet
    Upload Raptor 1B pretrained checkpoint at step 35000 29 days ago
  • tokenizer.vocab
    551 kB
    Upload Raptor 1B pretrained checkpoint at step 35000 29 days ago
  • tokenizer_config.json
    629 Bytes
    Update Raptor with SmolTalk instruction-tuned checkpoint at SFT step 7500 29 days ago