tensorblock/llama_16bit_2-GGUF

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Sasmitah/llama_16bit_2 - GGUF

This repo contains GGUF format model files for Sasmitah/llama_16bit_2.

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

Run them on the TensorBlock client using your local machine ↗

Prompt template

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 26 July 2024

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Model file specification

Filename	Quant type	File Size	Description
llama_16bit_2-Q2_K.gguf	Q2_K	0.581 GB	smallest, significant quality loss - not recommended for most purposes
llama_16bit_2-Q3_K_S.gguf	Q3_K_S	0.642 GB	very small, high quality loss
llama_16bit_2-Q3_K_M.gguf	Q3_K_M	0.691 GB	very small, high quality loss
llama_16bit_2-Q3_K_L.gguf	Q3_K_L	0.733 GB	small, substantial quality loss
llama_16bit_2-Q4_0.gguf	Q4_0	0.771 GB	legacy; small, very high quality loss - prefer using Q3_K_M
llama_16bit_2-Q4_K_S.gguf	Q4_K_S	0.776 GB	small, greater quality loss
llama_16bit_2-Q4_K_M.gguf	Q4_K_M	0.808 GB	medium, balanced quality - recommended
llama_16bit_2-Q5_0.gguf	Q5_0	0.893 GB	legacy; medium, balanced quality - prefer using Q4_K_M
llama_16bit_2-Q5_K_S.gguf	Q5_K_S	0.893 GB	large, low quality loss - recommended
llama_16bit_2-Q5_K_M.gguf	Q5_K_M	0.912 GB	large, very low quality loss - recommended
llama_16bit_2-Q6_K.gguf	Q6_K	1.022 GB	very large, extremely low quality loss
llama_16bit_2-Q8_0.gguf	Q8_0	1.321 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/llama_16bit_2-GGUF --include "llama_16bit_2-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/llama_16bit_2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'