Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

Skywalker1910
/
BB8

Text Generation
Transformers
Safetensors
PyTorch
English
language-model
from-scratch
lora
educational
gpt
decoder-only
Model card Files Files and versions
xet
Community

Instructions to use Skywalker1910/BB8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Skywalker1910/BB8 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Skywalker1910/BB8")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Skywalker1910/BB8", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Skywalker1910/BB8 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Skywalker1910/BB8"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Skywalker1910/BB8",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Skywalker1910/BB8
  • SGLang

    How to use Skywalker1910/BB8 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 "Skywalker1910/BB8" \
        --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": "Skywalker1910/BB8",
    		"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 "Skywalker1910/BB8" \
            --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": "Skywalker1910/BB8",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Skywalker1910/BB8 with Docker Model Runner:

    docker model run hf.co/Skywalker1910/BB8
BB8
1.72 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 30 commits
Skywalker1910's picture
Skywalker1910
Upload experiments/model_registry.jsonl with huggingface_hub
8d27305 verified 3 days ago
  • checkpoints
    Upload folder using huggingface_hub 3 days ago
  • configs
    Upload folder using huggingface_hub 3 days ago
  • data
    Upload folder using huggingface_hub 3 days ago
  • experiments
    Upload experiments/model_registry.jsonl with huggingface_hub 3 days ago
  • outputs
    Upload folder using huggingface_hub 3 days ago
  • .gitattributes
    1.87 kB
    Upload folder using huggingface_hub 3 days ago
  • README.md
    3.51 kB
    Upload README.md with huggingface_hub 3 days ago