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Multi-Domain-Expert-Learning
/
given-mpt-7b

Text Generation
Transformers
PyTorch
mpt
custom_code
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use Multi-Domain-Expert-Learning/given-mpt-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Multi-Domain-Expert-Learning/given-mpt-7b with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Multi-Domain-Expert-Learning/given-mpt-7b", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Multi-Domain-Expert-Learning/given-mpt-7b", trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained("Multi-Domain-Expert-Learning/given-mpt-7b", trust_remote_code=True)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Multi-Domain-Expert-Learning/given-mpt-7b 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/given-mpt-7b"
    # 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/given-mpt-7b",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Multi-Domain-Expert-Learning/given-mpt-7b
  • SGLang

    How to use Multi-Domain-Expert-Learning/given-mpt-7b 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/given-mpt-7b" \
        --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/given-mpt-7b",
    		"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/given-mpt-7b" \
            --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/given-mpt-7b",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Multi-Domain-Expert-Learning/given-mpt-7b with Docker Model Runner:

    docker model run hf.co/Multi-Domain-Expert-Learning/given-mpt-7b
given-mpt-7b
13.3 GB
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  • 2 contributors
History: 13 commits
Ontocord.AI
Upload adapt_tokenizer.py with huggingface_hub
787f7bb almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    8.42 kB
    Upload README.md with huggingface_hub almost 3 years ago
  • adapt_tokenizer.py
    1.75 kB
    Upload adapt_tokenizer.py with huggingface_hub almost 3 years ago
  • config.json
    1.23 kB
    Upload config.json with huggingface_hub almost 3 years ago
  • generation_config.json
    91 Bytes
    Upload MPTForCausalLM almost 3 years ago
  • hf_prefixlm_converter.py
    27.2 kB
    Upload hf_prefixlm_converter.py with huggingface_hub almost 3 years ago
  • modeling_mpt.py
    18.9 kB
    Create modeling_mpt.py almost 3 years ago
  • pytorch_model-00001-of-00002.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict",
    • "torch.BFloat16Storage"

    What is a pickle import?

    9.94 GB
    xet
    Upload MPTForCausalLM almost 3 years ago
  • pytorch_model-00002-of-00002.bin

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch.BFloat16Storage",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    3.36 GB
    xet
    Upload MPTForCausalLM almost 3 years ago
  • pytorch_model.bin.index.json
    16 kB
    Upload MPTForCausalLM almost 3 years ago
  • special_tokens_map.json
    99 Bytes
    Upload tokenizer almost 3 years ago
  • tokenizer.json
    2.11 MB
    Upload tokenizer almost 3 years ago
  • tokenizer_config.json
    238 Bytes
    Upload tokenizer almost 3 years ago