tensorblock/FastApply-1.5B-v1.0-GGUF

Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server

Kortix/FastApply-1.5B-v1.0 - GGUF

This repo contains GGUF format model files for Kortix/FastApply-1.5B-v1.0.

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

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Model file specification

Filename	Quant type	File Size	Description
FastApply-1.5B-v1.0-Q2_K.gguf	Q2_K	0.676 GB	smallest, significant quality loss - not recommended for most purposes
FastApply-1.5B-v1.0-Q3_K_S.gguf	Q3_K_S	0.761 GB	very small, high quality loss
FastApply-1.5B-v1.0-Q3_K_M.gguf	Q3_K_M	0.824 GB	very small, high quality loss
FastApply-1.5B-v1.0-Q3_K_L.gguf	Q3_K_L	0.880 GB	small, substantial quality loss
FastApply-1.5B-v1.0-Q4_0.gguf	Q4_0	0.935 GB	legacy; small, very high quality loss - prefer using Q3_K_M
FastApply-1.5B-v1.0-Q4_K_S.gguf	Q4_K_S	0.940 GB	small, greater quality loss
FastApply-1.5B-v1.0-Q4_K_M.gguf	Q4_K_M	0.986 GB	medium, balanced quality - recommended
FastApply-1.5B-v1.0-Q5_0.gguf	Q5_0	1.099 GB	legacy; medium, balanced quality - prefer using Q4_K_M
FastApply-1.5B-v1.0-Q5_K_S.gguf	Q5_K_S	1.099 GB	large, low quality loss - recommended
FastApply-1.5B-v1.0-Q5_K_M.gguf	Q5_K_M	1.125 GB	large, very low quality loss - recommended
FastApply-1.5B-v1.0-Q6_K.gguf	Q6_K	1.273 GB	very large, extremely low quality loss
FastApply-1.5B-v1.0-Q8_0.gguf	Q8_0	1.647 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/FastApply-1.5B-v1.0-GGUF --include "FastApply-1.5B-v1.0-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/FastApply-1.5B-v1.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'