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tzervas
/
bwsk-gpt2-medium

Text Generation
Transformers
Safetensors
bwsk
combinator-analysis
transformer
reversible-backprop
convergence-training
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use tzervas/bwsk-gpt2-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use tzervas/bwsk-gpt2-medium with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="tzervas/bwsk-gpt2-medium")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("tzervas/bwsk-gpt2-medium", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use tzervas/bwsk-gpt2-medium with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "tzervas/bwsk-gpt2-medium"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "tzervas/bwsk-gpt2-medium",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/tzervas/bwsk-gpt2-medium
  • SGLang

    How to use tzervas/bwsk-gpt2-medium 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 "tzervas/bwsk-gpt2-medium" \
        --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": "tzervas/bwsk-gpt2-medium",
    		"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 "tzervas/bwsk-gpt2-medium" \
            --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": "tzervas/bwsk-gpt2-medium",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use tzervas/bwsk-gpt2-medium with Docker Model Runner:

    docker model run hf.co/tzervas/bwsk-gpt2-medium
bwsk-gpt2-medium
8.54 GB
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  • 1 contributor
History: 16 commits
tzervas's picture
tzervas
Add BWSK model card
fdcd063 verified 3 months ago
  • finetune-bwsk-analyzed
    Add finetune-bwsk-analyzed training results 3 months ago
  • finetune-bwsk-reversible
    Add finetune-bwsk-reversible training results 3 months ago
  • finetune-conventional
    Add finetune-conventional training results 3 months ago
  • scratch-bwsk-analyzed
    Add scratch-bwsk-analyzed training results 3 months ago
  • scratch-bwsk-reversible
    Add scratch-bwsk-reversible training results 3 months ago
  • scratch-conventional
    Add scratch-conventional training results 3 months ago
  • .gitattributes
    1.52 kB
    initial commit 3 months ago
  • README.md
    6.31 kB
    Add BWSK model card 3 months ago
  • results.json
    411 kB
    Add aggregated training results 3 months ago