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Deci
/
DeciLM-6b-instruct

Text Generation
Transformers
Safetensors
English
Deci AI
DeciLM
Instruction
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community
7

Instructions to use Deci/DeciLM-6b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Deci/DeciLM-6b-instruct with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Deci/DeciLM-6b-instruct", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("Deci/DeciLM-6b-instruct", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Deci/DeciLM-6b-instruct with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Deci/DeciLM-6b-instruct"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Deci/DeciLM-6b-instruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Deci/DeciLM-6b-instruct
  • SGLang

    How to use Deci/DeciLM-6b-instruct 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 "Deci/DeciLM-6b-instruct" \
        --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": "Deci/DeciLM-6b-instruct",
    		"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 "Deci/DeciLM-6b-instruct" \
            --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": "Deci/DeciLM-6b-instruct",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Deci/DeciLM-6b-instruct with Docker Model Runner:

    docker model run hf.co/Deci/DeciLM-6b-instruct
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

could we add model_type in config.json like https://huggingface.co/Deci/DeciLM-7B/commit/0be2d64c57344399a148a5f9e9129b7d6a07aac0

#7 opened almost 2 years ago by
sywangyi

GGUF version please

2
#5 opened over 2 years ago by
Hoioi

pipeline("text-generation") + batch_size > 1 results in `For support of custom attention masks`...

#4 opened over 2 years ago by
michael-newsrx-com

fine tune code

1
#2 opened over 2 years ago by
bharathrajcl
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