Pomni/distil-large-v3.5-ggml-allquants

Distil-Large-v3.5 quants

This is a repository of GGML quants for distil-large-v3.5 (a Whisper-based transcription model), for use with whisper.cpp.

If you are looking for a program to run this model with, then I would recommend EasyWhisper UI, as it is user-friendly, has a GUI, and automates a lot of the hard stuff for you.

List of Quants

Clicking on a link will download the corresponding quant instantly.

Link	Quant	Size	Notes
GGML	F32	3.03 GB	Likely overkill.
GGML	F16	1.52 GB	Performs better than Q8_0 for noisy audio and music.
GGML	Q8_0	818 MB	Sweet spot; superficial quality loss at nearly double the speed.
GGML	Q6_K	637 MB
GGML	Q5_K	538 MB
GGML	Q5_1	585 MB
GGML	Q5_0	538 MB	Last "good" quant; anything below loses quality rapidly.
GGML	Q4_K	444 MB	Might not have lost too much quality, but I'm not sure.
GGML	Q4_1	491 MB
GGML	Q4_0	444 MB
GGML	Q3_K	345 MB
GGML	Q2_K	269 MB	Completely non-sensical output.

The F16 quant was taken from distil-whisper/distil-large-v3.5-ggml/ggml-model.bin.

Questions you may have

Why do the "K-quants" not work for me?

My guess is that your GPU might be too old to recognize them, considering that I have gotten the same error on my GTX 1080. If you would like to run them regardless, you can try switching to CPU inference.

Are the K-quants "S", "M", or "L"?

The quantizer I was using was not specific about this, so I do not know about this either.

What program did you use to make these quants?

I used whisper.cpp v1.7.6 on Windows x64, leveraging CUDA 12.4.0. For the F32 quant, I converted the original Hugging Face (H5) format model to a GGML using the models/convert-h5-to-ggml.py script.

One or multiple of the quants are not working for me.

Open a new discussion in the community tab about this, and I will look into the issue.