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tensorblock/
awaisakhtar_Llama-2-7b-summarization-finetuned-on-xsum-GGUF

Name: awaisakhtar_Llama-2-7b-summarization-finetuned-on-xsum-GGUF
Availability: InStock

awaisakhtar/Llama-2-7b-summarization-finetuned-on-xsum

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awaisakhtar/Llama-2-7b-summarization-finetuned-on-xsum - GGUF

This repo contains GGUF format model files for awaisakhtar/Llama-2-7b-summarization-finetuned-on-xsum.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.

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Model file specification

Filename	Quant type	File Size	Description
Llama-2-7b-summarization-finetuned-on-xsum-Q2_K.gguf	Q2_K	2.533 GB	smallest, significant quality loss - not recommended for most purposes
Llama-2-7b-summarization-finetuned-on-xsum-Q3_K_S.gguf	Q3_K_S	2.948 GB	very small, high quality loss
Llama-2-7b-summarization-finetuned-on-xsum-Q3_K_M.gguf	Q3_K_M	3.298 GB	very small, high quality loss
Llama-2-7b-summarization-finetuned-on-xsum-Q3_K_L.gguf	Q3_K_L	3.597 GB	small, substantial quality loss
Llama-2-7b-summarization-finetuned-on-xsum-Q4_0.gguf	Q4_0	3.826 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Llama-2-7b-summarization-finetuned-on-xsum-Q4_K_S.gguf	Q4_K_S	3.857 GB	small, greater quality loss
Llama-2-7b-summarization-finetuned-on-xsum-Q4_K_M.gguf	Q4_K_M	4.081 GB	medium, balanced quality - recommended
Llama-2-7b-summarization-finetuned-on-xsum-Q5_0.gguf	Q5_0	4.652 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Llama-2-7b-summarization-finetuned-on-xsum-Q5_K_S.gguf	Q5_K_S	4.652 GB	large, low quality loss - recommended
Llama-2-7b-summarization-finetuned-on-xsum-Q5_K_M.gguf	Q5_K_M	4.783 GB	large, very low quality loss - recommended
Llama-2-7b-summarization-finetuned-on-xsum-Q6_K.gguf	Q6_K	5.529 GB	very large, extremely low quality loss
Llama-2-7b-summarization-finetuned-on-xsum-Q8_0.gguf	Q8_0	7.161 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/awaisakhtar_Llama-2-7b-summarization-finetuned-on-xsum-GGUF --include "Llama-2-7b-summarization-finetuned-on-xsum-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/awaisakhtar_Llama-2-7b-summarization-finetuned-on-xsum-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'