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Caiyun-AI
/
MUDDFormer-2.8B

Text Generation
Transformers
Safetensors
PyTorch
English
muddformer
causal-lm
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use Caiyun-AI/MUDDFormer-2.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Caiyun-AI/MUDDFormer-2.8B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Caiyun-AI/MUDDFormer-2.8B", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("Caiyun-AI/MUDDFormer-2.8B", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Caiyun-AI/MUDDFormer-2.8B with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Caiyun-AI/MUDDFormer-2.8B"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Caiyun-AI/MUDDFormer-2.8B",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Caiyun-AI/MUDDFormer-2.8B
  • SGLang

    How to use Caiyun-AI/MUDDFormer-2.8B 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 "Caiyun-AI/MUDDFormer-2.8B" \
        --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": "Caiyun-AI/MUDDFormer-2.8B",
    		"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 "Caiyun-AI/MUDDFormer-2.8B" \
            --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": "Caiyun-AI/MUDDFormer-2.8B",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Caiyun-AI/MUDDFormer-2.8B with Docker Model Runner:

    docker model run hf.co/Caiyun-AI/MUDDFormer-2.8B
MUDDFormer-2.8B
5.61 GB
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  • 3 contributors
History: 10 commits
Hilbertmeng's picture
Hilbertmeng
remove stack_hidden
f6626e7 about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    2.87 kB
    Add transformers library and text-generation pipeline to model card (#1) over 1 year ago
  • config.json
    804 Bytes
    remove stack_hidden about 1 year ago
  • configuration_muddformer.py
    2.58 kB
    remove stack_hidden about 1 year ago
  • generation_demo.py
    1.92 kB
    fix typo & dtype over 1 year ago
  • model-00001-of-00002.safetensors
    4.97 GB
    xet
    add model weights over 1 year ago
  • model-00002-of-00002.safetensors
    643 MB
    xet
    add model weights over 1 year ago
  • model.safetensors.index.json
    37.4 kB
    add model weights over 1 year ago
  • modeling_muddformer.py
    19.3 kB
    remove stack_hidden about 1 year ago
  • requirements.txt
    48 Bytes
    fix requirements over 1 year ago
  • tokenizer.json
    2.11 MB
    add code over 1 year ago
  • tokenizer_config.json
    264 Bytes
    add code over 1 year ago