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tensorblock/
llama2.c-stories42M-pruned2.4-GGUF

Name: llama2.c-stories42M-pruned2.4-GGUF
Availability: InStock

nm-testing/llama2.c-stories42M-pruned2.4

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nm-testing/llama2.c-stories42M-pruned2.4 - GGUF

This repo contains GGUF format model files for nm-testing/llama2.c-stories42M-pruned2.4.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

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## Prompt template

Model file specification

Filename	Quant type	File Size	Description
llama2.c-stories42M-pruned2.4-Q2_K.gguf	Q2_K	0.030 GB	smallest, significant quality loss - not recommended for most purposes
llama2.c-stories42M-pruned2.4-Q3_K_S.gguf	Q3_K_S	0.033 GB	very small, high quality loss
llama2.c-stories42M-pruned2.4-Q3_K_M.gguf	Q3_K_M	0.034 GB	very small, high quality loss
llama2.c-stories42M-pruned2.4-Q3_K_L.gguf	Q3_K_L	0.035 GB	small, substantial quality loss
llama2.c-stories42M-pruned2.4-Q4_0.gguf	Q4_0	0.038 GB	legacy; small, very high quality loss - prefer using Q3_K_M
llama2.c-stories42M-pruned2.4-Q4_K_S.gguf	Q4_K_S	0.039 GB	small, greater quality loss
llama2.c-stories42M-pruned2.4-Q4_K_M.gguf	Q4_K_M	0.040 GB	medium, balanced quality - recommended
llama2.c-stories42M-pruned2.4-Q5_0.gguf	Q5_0	0.043 GB	legacy; medium, balanced quality - prefer using Q4_K_M
llama2.c-stories42M-pruned2.4-Q5_K_S.gguf	Q5_K_S	0.043 GB	large, low quality loss - recommended
llama2.c-stories42M-pruned2.4-Q5_K_M.gguf	Q5_K_M	0.044 GB	large, very low quality loss - recommended
llama2.c-stories42M-pruned2.4-Q6_K.gguf	Q6_K	0.050 GB	very large, extremely low quality loss
llama2.c-stories42M-pruned2.4-Q8_0.gguf	Q8_0	0.062 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/llama2.c-stories42M-pruned2.4-GGUF --include "llama2.c-stories42M-pruned2.4-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/llama2.c-stories42M-pruned2.4-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'