Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

umm-maybe
/
StackStar_GPT2

Text Generation
Transformers
PyTorch
gpt2
text-generation-inference
Model card Files Files and versions
xet
Community
2

Instructions to use umm-maybe/StackStar_GPT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use umm-maybe/StackStar_GPT2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="umm-maybe/StackStar_GPT2")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("umm-maybe/StackStar_GPT2")
    model = AutoModelForCausalLM.from_pretrained("umm-maybe/StackStar_GPT2")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use umm-maybe/StackStar_GPT2 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "umm-maybe/StackStar_GPT2"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "umm-maybe/StackStar_GPT2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/umm-maybe/StackStar_GPT2
  • SGLang

    How to use umm-maybe/StackStar_GPT2 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 "umm-maybe/StackStar_GPT2" \
        --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": "umm-maybe/StackStar_GPT2",
    		"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 "umm-maybe/StackStar_GPT2" \
            --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": "umm-maybe/StackStar_GPT2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use umm-maybe/StackStar_GPT2 with Docker Model Runner:

    docker model run hf.co/umm-maybe/StackStar_GPT2
StackStar_GPT2
1.45 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
umm-maybe's picture
umm-maybe
Upload 9 files
2bc41f2 almost 3 years ago
  • .gitattributes
    1.48 kB
    initial commit almost 3 years ago
  • config.json
    967 Bytes
    Upload 10 files almost 3 years ago
  • eval_results.txt
    60 Bytes
    Upload 10 files almost 3 years ago
  • generation_config.json
    119 Bytes
    Upload 10 files almost 3 years ago
  • merges.txt
    456 kB
    Upload 10 files almost 3 years ago
  • model_args.json
    2.78 kB
    Upload 10 files almost 3 years ago
  • pytorch_model.bin
    1.44 GB
    xet
    Upload 10 files almost 3 years ago
  • special_tokens_map.json
    438 Bytes
    Upload 10 files almost 3 years ago
  • tokenizer_config.json
    727 Bytes
    Upload 10 files almost 3 years ago
  • training_args.bin
    3.39 kB
    xet
    Upload 10 files almost 3 years ago
  • vocab.json
    999 kB
    Upload 10 files almost 3 years ago