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HuggingFaceTB
/
cosmo-1b

Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
Model card Files Files and versions
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Community
8

Instructions to use HuggingFaceTB/cosmo-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use HuggingFaceTB/cosmo-1b with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="HuggingFaceTB/cosmo-1b")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmo-1b")
    model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/cosmo-1b")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use HuggingFaceTB/cosmo-1b with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "HuggingFaceTB/cosmo-1b"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "HuggingFaceTB/cosmo-1b",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/HuggingFaceTB/cosmo-1b
  • SGLang

    How to use HuggingFaceTB/cosmo-1b 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 "HuggingFaceTB/cosmo-1b" \
        --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": "HuggingFaceTB/cosmo-1b",
    		"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 "HuggingFaceTB/cosmo-1b" \
            --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": "HuggingFaceTB/cosmo-1b",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use HuggingFaceTB/cosmo-1b with Docker Model Runner:

    docker model run hf.co/HuggingFaceTB/cosmo-1b
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

add AIBOM

#8 opened 11 months ago by
RiccardoDav

Dataset for reproduction

➕ 1
#7 opened about 2 years ago by
ahans1

Training script for cosmo-1b?

4
#6 opened about 2 years ago by
vdmbrsv

Adding Evaluation Results

#5 opened about 2 years ago by
leaderboard-pr-bot

No chat template added

1
#4 opened about 2 years ago by
lcahill

What is the command used to evaluate on MMLU?

2
#3 opened about 2 years ago by
PY007

Is this model trained from scratch (randomly init) or layers or weights of other models used during the training?

1
#2 opened about 2 years ago by
Sunny111

Congrats 🎉

❤️ 4
1
#1 opened about 2 years ago by
mrm8488
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