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tensorblock/
fblgit_una-cybertron-7b-v1-fp16-GGUF

Name: fblgit_una-cybertron-7b-v1-fp16-GGUF
Availability: InStock

fblgit/una-cybertron-7b-v1-fp16

quantized

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HuggingFaceH4/ultrafeedback_binarized

Open-Orca/SlimOrca-Dedup

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fblgit/una-cybertron-7b-v1-fp16 - GGUF

This repo contains GGUF format model files for fblgit/una-cybertron-7b-v1-fp16.

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
una-cybertron-7b-v1-fp16-Q2_K.gguf	Q2_K	2.719 GB	smallest, significant quality loss - not recommended for most purposes
una-cybertron-7b-v1-fp16-Q3_K_S.gguf	Q3_K_S	3.165 GB	very small, high quality loss
una-cybertron-7b-v1-fp16-Q3_K_M.gguf	Q3_K_M	3.519 GB	very small, high quality loss
una-cybertron-7b-v1-fp16-Q3_K_L.gguf	Q3_K_L	3.822 GB	small, substantial quality loss
una-cybertron-7b-v1-fp16-Q4_0.gguf	Q4_0	4.109 GB	legacy; small, very high quality loss - prefer using Q3_K_M
una-cybertron-7b-v1-fp16-Q4_K_S.gguf	Q4_K_S	4.140 GB	small, greater quality loss
una-cybertron-7b-v1-fp16-Q4_K_M.gguf	Q4_K_M	4.368 GB	medium, balanced quality - recommended
una-cybertron-7b-v1-fp16-Q5_0.gguf	Q5_0	4.998 GB	legacy; medium, balanced quality - prefer using Q4_K_M
una-cybertron-7b-v1-fp16-Q5_K_S.gguf	Q5_K_S	4.998 GB	large, low quality loss - recommended
una-cybertron-7b-v1-fp16-Q5_K_M.gguf	Q5_K_M	5.131 GB	large, very low quality loss - recommended
una-cybertron-7b-v1-fp16-Q6_K.gguf	Q6_K	5.942 GB	very large, extremely low quality loss
una-cybertron-7b-v1-fp16-Q8_0.gguf	Q8_0	7.696 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/fblgit_una-cybertron-7b-v1-fp16-GGUF --include "una-cybertron-7b-v1-fp16-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/fblgit_una-cybertron-7b-v1-fp16-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'