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BeardedMonster
/
SabiYarn-125M

Text Generation
Transformers
Safetensors
nanogpt-j
custom_code
Model card Files Files and versions
xet
Community
2

Instructions to use BeardedMonster/SabiYarn-125M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use BeardedMonster/SabiYarn-125M with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="BeardedMonster/SabiYarn-125M", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("BeardedMonster/SabiYarn-125M", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use BeardedMonster/SabiYarn-125M with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "BeardedMonster/SabiYarn-125M"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "BeardedMonster/SabiYarn-125M",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/BeardedMonster/SabiYarn-125M
  • SGLang

    How to use BeardedMonster/SabiYarn-125M 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 "BeardedMonster/SabiYarn-125M" \
        --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": "BeardedMonster/SabiYarn-125M",
    		"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 "BeardedMonster/SabiYarn-125M" \
            --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": "BeardedMonster/SabiYarn-125M",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use BeardedMonster/SabiYarn-125M with Docker Model Runner:

    docker model run hf.co/BeardedMonster/SabiYarn-125M
SabiYarn-125M
556 MB
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  • 1 contributor
History: 25 commits
BeardedMonster's picture
BeardedMonster
fix error
690b237 verified 3 months ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • README.md
    11.8 kB
    Update README.md over 1 year ago
  • config.json
    435 Bytes
    Update config.json to support kv_cache 3 months ago
  • generation_config.json
    69 Bytes
    Upload GPTJXForCausalLM over 1 year ago
  • model.safetensors
    553 MB
    xet
    Upload GPTJXForCausalLM almost 2 years ago
  • pretrained_config.py
    975 Bytes
    Upload GPTJXForCausalLM almost 2 years ago
  • pretrained_model.py
    23.6 kB
    fix error 3 months ago
  • special_tokens_map.json
    635 Bytes
    Upload tokenizer almost 2 years ago
  • tokenizer.json
    2.26 MB
    Upload tokenizer almost 2 years ago
  • tokenizer_config.json
    8.57 kB
    Upload tokenizer almost 2 years ago