How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BasedBots/cosmo-1b-Q4_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BasedBots/cosmo-1b-Q4_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker
docker model run hf.co/BasedBots/cosmo-1b-Q4_K_M-GGUF:Q4_K_M
Quick Links

BasedBots/cosmo-1b-Q4_K_M-GGUF

This model was converted to GGUF format from HuggingFaceTB/cosmo-1b using llama.cpp. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew.

brew install ggerganov/ggerganov/llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo BasedBots/cosmo-1b-Q4_K_M-GGUF --model cosmo-1b.Q4_K_M.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo BasedBots/cosmo-1b-Q4_K_M-GGUF --model cosmo-1b.Q4_K_M.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

git clone https://github.com/ggerganov/llama.cpp &&             cd llama.cpp &&             make &&             ./main -m cosmo-1b.Q4_K_M.gguf -n 128
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GGUF
Model size
2B params
Architecture
llama
Hardware compatibility
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Dataset used to train BasedBots/cosmo-1b-Q4_K_M-GGUF

Collection including BasedBots/cosmo-1b-Q4_K_M-GGUF