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ibm-granite/granite-34b-code-base-8k - GGUF

This repo contains GGUF format model files for ibm-granite/granite-34b-code-base-8k.

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

Prompt template


Model file specification

Filename Quant type File Size Description
granite-34b-code-base-8k-Q2_K.gguf Q2_K 12.207 GB smallest, significant quality loss - not recommended for most purposes
granite-34b-code-base-8k-Q3_K_S.gguf Q3_K_S 13.791 GB very small, high quality loss
granite-34b-code-base-8k-Q3_K_M.gguf Q3_K_M 16.361 GB very small, high quality loss
granite-34b-code-base-8k-Q3_K_L.gguf Q3_K_L 18.207 GB small, substantial quality loss
granite-34b-code-base-8k-Q4_0.gguf Q4_0 17.917 GB legacy; small, very high quality loss - prefer using Q3_K_M
granite-34b-code-base-8k-Q4_K_S.gguf Q4_K_S 18.110 GB small, greater quality loss
granite-34b-code-base-8k-Q4_K_M.gguf Q4_K_M 19.915 GB medium, balanced quality - recommended
granite-34b-code-base-8k-Q5_0.gguf Q5_0 21.800 GB legacy; medium, balanced quality - prefer using Q4_K_M
granite-34b-code-base-8k-Q5_K_S.gguf Q5_K_S 21.800 GB large, low quality loss - recommended
granite-34b-code-base-8k-Q5_K_M.gguf Q5_K_M 23.050 GB large, very low quality loss - recommended
granite-34b-code-base-8k-Q6_K.gguf Q6_K 25.926 GB very large, extremely low quality loss
granite-34b-code-base-8k-Q8_0.gguf Q8_0 33.518 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/granite-34b-code-base-8k-GGUF --include "granite-34b-code-base-8k-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/granite-34b-code-base-8k-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
starcoder

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Datasets used to train tensorblock/granite-34b-code-base-8k-GGUF

Evaluation results