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 Settings
- 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
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<s> Photosynthesis is the process by which green plants, algae and some bacteria convert light energy into chemical energy in order to fuel their metabolic processes. The reaction takes place within specialized cells called chloroplasts. This article focuses on the electron transport chain (ETC), a critical part of photosystem II where most of the solar-driven electrons are passed through before being reduced to water.
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# Limitations
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<s> Photosynthesis is the process by which green plants, algae and some bacteria convert light energy into chemical energy in order to fuel their metabolic processes. The reaction takes place within specialized cells called chloroplasts. This article focuses on the electron transport chain (ETC), a critical part of photosystem II where most of the solar-driven electrons are passed through before being reduced to water.
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# Evaluation
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Below are the evluation results of Cosmo-1B. The model is better than TinyLlama 1.1B on ARC-easy, ARC-challenge, OpenBookQA and MMLU, and has comparable performance to Qwen-1.5-1B on ARC-easy, ARC-challenge and OpenBookQA.
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However, we notice some perfoamnce gaps compared to Phi-1.5 suggesting a better synthetic generation quality which can be related to the LLM used for generation, topic coverage or prompts.
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# Limitations
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