semcoder-GGUF / README.md
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metadata
license: mit
tags:
  - TensorBlock
  - GGUF
base_model: semcoder/semcoder
TensorBlock

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semcoder/semcoder - GGUF

This repo contains GGUF format model files for semcoder/semcoder.

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
semcoder-Q2_K.gguf Q2_K 2.535 GB smallest, significant quality loss - not recommended for most purposes
semcoder-Q3_K_S.gguf Q3_K_S 2.950 GB very small, high quality loss
semcoder-Q3_K_M.gguf Q3_K_M 3.300 GB very small, high quality loss
semcoder-Q3_K_L.gguf Q3_K_L 3.599 GB small, substantial quality loss
semcoder-Q4_0.gguf Q4_0 3.828 GB legacy; small, very high quality loss - prefer using Q3_K_M
semcoder-Q4_K_S.gguf Q4_K_S 3.859 GB small, greater quality loss
semcoder-Q4_K_M.gguf Q4_K_M 4.083 GB medium, balanced quality - recommended
semcoder-Q5_0.gguf Q5_0 4.654 GB legacy; medium, balanced quality - prefer using Q4_K_M
semcoder-Q5_K_S.gguf Q5_K_S 4.654 GB large, low quality loss - recommended
semcoder-Q5_K_M.gguf Q5_K_M 4.785 GB large, very low quality loss - recommended
semcoder-Q6_K.gguf Q6_K 5.531 GB very large, extremely low quality loss
semcoder-Q8_0.gguf Q8_0 7.164 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/semcoder-GGUF --include "semcoder-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/semcoder-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'