Text Generation
Transformers
GGUF
English
code
llama-cpp
gguf-my-repo
Eval Results (legacy)
How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Netsnake/Refact-1_6B-fim-Q5_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": "Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker
docker model run hf.co/Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M
Quick Links

Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF

This model was converted to GGUF format from refactai/Refact-1_6B-fim using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF --hf-file refact-1_6b-fim-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF --hf-file refact-1_6b-fim-q5_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.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF --hf-file refact-1_6b-fim-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF --hf-file refact-1_6b-fim-q5_k_m.gguf -c 2048
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GGUF
Model size
2B params
Architecture
refact
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