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Multi-Domain-Expert-Learning
/
expert-github

Text Generation
Transformers
PyTorch
gpt_neox
Generated from Trainer
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use Multi-Domain-Expert-Learning/expert-github with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Multi-Domain-Expert-Learning/expert-github with Transformers:

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

    How to use Multi-Domain-Expert-Learning/expert-github with vLLM:

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

    How to use Multi-Domain-Expert-Learning/expert-github 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 "Multi-Domain-Expert-Learning/expert-github" \
        --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": "Multi-Domain-Expert-Learning/expert-github",
    		"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 "Multi-Domain-Expert-Learning/expert-github" \
            --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": "Multi-Domain-Expert-Learning/expert-github",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Multi-Domain-Expert-Learning/expert-github with Docker Model Runner:

    docker model run hf.co/Multi-Domain-Expert-Learning/expert-github
expert-github
4.12 GB
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  • 2 contributors
History: 6 commits
cabbage972's picture
cabbage972
End of training
7ae9cf6 about 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • .gitignore
    13 Bytes
    Training in progress, step 500 about 3 years ago
  • README.md
    1.73 kB
    update model card README.md about 3 years ago
  • all_results.json
    446 Bytes
    End of training about 3 years ago
  • config.json
    620 Bytes
    Training in progress, step 500 about 3 years ago
  • eval_results.json
    271 Bytes
    End of training about 3 years ago
  • generation_config.json
    111 Bytes
    Model save about 3 years ago
  • pytorch_model.bin
    4.11 GB
    xet
    Training in progress, step 1000 about 3 years ago
  • special_tokens_map.json
    99 Bytes
    Training in progress, step 500 about 3 years ago
  • tokenizer.json
    2.11 MB
    Training in progress, step 500 about 3 years ago
  • tokenizer_config.json
    264 Bytes
    Training in progress, step 500 about 3 years ago
  • train_results.json
    196 Bytes
    End of training about 3 years ago
  • trainer_state.json
    7.38 kB
    End of training about 3 years ago
  • training_args.bin
    3.71 kB
    xet
    Training in progress, step 500 about 3 years ago