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codellama/CodeLlama-34b-Python-hf - GGUF

This repo contains GGUF format model files for codellama/CodeLlama-34b-Python-hf.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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Model file specification

Filename Quant type File Size Description
CodeLlama-34b-Python-hf-Q2_K.gguf Q2_K 11.647 GB smallest, significant quality loss - not recommended for most purposes
CodeLlama-34b-Python-hf-Q3_K_S.gguf Q3_K_S 13.602 GB very small, high quality loss
CodeLlama-34b-Python-hf-Q3_K_M.gguf Q3_K_M 15.186 GB very small, high quality loss
CodeLlama-34b-Python-hf-Q3_K_L.gguf Q3_K_L 16.551 GB small, substantial quality loss
CodeLlama-34b-Python-hf-Q4_0.gguf Q4_0 17.744 GB legacy; small, very high quality loss - prefer using Q3_K_M
CodeLlama-34b-Python-hf-Q4_K_S.gguf Q4_K_S 17.873 GB small, greater quality loss
CodeLlama-34b-Python-hf-Q4_K_M.gguf Q4_K_M 18.831 GB medium, balanced quality - recommended
CodeLlama-34b-Python-hf-Q5_0.gguf Q5_0 21.641 GB legacy; medium, balanced quality - prefer using Q4_K_M
CodeLlama-34b-Python-hf-Q5_K_S.gguf Q5_K_S 21.641 GB large, low quality loss - recommended
CodeLlama-34b-Python-hf-Q5_K_M.gguf Q5_K_M 22.202 GB large, very low quality loss - recommended
CodeLlama-34b-Python-hf-Q6_K.gguf Q6_K 25.783 GB very large, extremely low quality loss
CodeLlama-34b-Python-hf-Q8_0.gguf Q8_0 33.394 GB very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/CodeLlama-34b-Python-hf-GGUF --include "CodeLlama-34b-Python-hf-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/CodeLlama-34b-Python-hf-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
Downloads last month
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GGUF
Model size
33.7B params
Architecture
llama

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Inference Examples
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