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Nethermind
/
Mpt-Instruct-DotNet-XS

Text Generation
Transformers
PyTorch
English
mosaic_gpt
csharp
mpt
instruct
1b
llm
.net
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use Nethermind/Mpt-Instruct-DotNet-XS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Nethermind/Mpt-Instruct-DotNet-XS with Transformers:

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

    How to use Nethermind/Mpt-Instruct-DotNet-XS with vLLM:

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

    How to use Nethermind/Mpt-Instruct-DotNet-XS 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 "Nethermind/Mpt-Instruct-DotNet-XS" \
        --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": "Nethermind/Mpt-Instruct-DotNet-XS",
    		"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 "Nethermind/Mpt-Instruct-DotNet-XS" \
            --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": "Nethermind/Mpt-Instruct-DotNet-XS",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Nethermind/Mpt-Instruct-DotNet-XS with Docker Model Runner:

    docker model run hf.co/Nethermind/Mpt-Instruct-DotNet-XS
Mpt-Instruct-DotNet-XS
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  • 1 contributor
History: 5 commits
Kabumbus's picture
Kabumbus
GGML models that can run f16 41.68 ms per token and q8 23.76 ms per token giving good results
56d7c99 over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    3.78 kB
    Usage example over 2 years ago
  • attention.py
    13.8 kB
    Trained model over 2 years ago
  • config.json
    1.08 kB
    Trained model over 2 years ago
  • configuration_mosaic_gpt.py
    8.87 kB
    Trained model over 2 years ago
  • generation_config.json
    91 Bytes
    Trained model over 2 years ago
  • ggml-model-f16.bin
    2.62 GB
    xet
    GGML models that can run f16 41.68 ms per token and q8 23.76 ms per token giving good results over 2 years ago
  • ggml-model-q8_0.bin
    1.39 GB
    xet
    GGML models that can run f16 41.68 ms per token and q8 23.76 ms per token giving good results over 2 years ago
  • gpt_blocks.py
    3.11 kB
    Trained model over 2 years ago
  • low_precision_layernorm.py
    1.27 kB
    Trained model over 2 years ago
  • mosaic_gpt.py
    19.5 kB
    Trained model over 2 years ago
  • param_init_fns.py
    15.9 kB
    Trained model over 2 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    2.62 GB
    xet
    Trained model over 2 years ago
  • special_tokens_map.json
    131 Bytes
    Trained model over 2 years ago
  • tokenizer.json
    2.11 MB
    Trained model over 2 years ago
  • tokenizer_config.json
    264 Bytes
    Trained model over 2 years ago