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distil-medium.en-ggml-allquants
Pomni/distil-medium.en-ggml-allquants
distil-medium.en-ggml-allquants is a automatic speech recognition model from Pomni. Use it when you need speech turned into text. The card lists the license as mit.
This is a repository of GGML quants for distil-medium.en (a Whisper-based transcription model), for use with whisper.cpp.
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Public
Updated Aug 15, 2025
Repo size
5.1 GB
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Repo makeup
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.bin5.1 GB · 100%
At a glance
- Task
- Automatic Speech Recognition
- License
- mit
- Access
- Public
- Created
- Aug 8, 2025
- Updated
- Aug 15, 2025
- SHA
- 9c579114
Base models
Model card
From the Hugging Face model README
Distil-Medium.en quants
This is a repository of GGML quants for distil-medium.en (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 | 1.58 GB | Likely overkill. |
| GGML | F16 | 794 MB | Performs better than Q8_0 for noisy audio and music. |
| GGML | Q8_0 | 430 MB | Sweet spot; superficial quality loss at nearly double the speed. |
| GGML | Q6_K | 336 MB | |
| GGML | Q5_K | 284 MB | |
| GGML | Q5_1 | 308 MB | |
| GGML | Q5_0 | 284 MB | Last "good" quant; anything below loses quality rapidly. |
| GGML | Q4_K | 235 MB | Might not have lost too much quality, but I'm not sure. |
| GGML | Q4_1 | 260 MB | |
| GGML | Q4_0 | 235 MB | |
| GGML | Q3_K | 184 MB | |
| GGML | Q2_K | 144 MB | Completely non-sensical outputs. |
The F32 quant was taken from distil-whisper/distil-medium.en/ggml-medium-32-2.en.fp32.bin, and the F16 quant was taken from distil-whisper/distil-medium.en/ggml-medium-32-2.en.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.
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.
- Task
- Automatic Speech Recognition
- License
- mit
- Languages
- en
- Created
- Aug 8, 2025
- Updated
- Aug 15, 2025