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--- |
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license: mit |
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quantized_by: Pomni |
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language: |
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- en |
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base_model: |
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- distil-whisper/distil-large-v3.5 |
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pipeline_tag: automatic-speech-recognition |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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- openslr/librispeech_asr |
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- facebook/voxpopuli |
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- LIUM/tedlium |
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- MLCommons/peoples_speech |
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- speechcolab/gigaspeech |
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- edinburghcstr/ami |
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- espnet/yodas |
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tags: |
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- whisper.cpp |
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- ggml |
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- whisper |
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- audio |
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- speech |
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- voice |
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- distil |
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--- |
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# Distil-Large-v3.5 quants |
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This is a repository of **GGML quants for [distil-large-v3.5](https://huggingface.co/distil-whisper/distil-large-v3.5)** (a Whisper-based transcription model), for use with [whisper.cpp](https://github.com/ggml-org/whisper.cpp). |
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If you are looking for a program to run this model with, then I would recommend [EasyWhisper UI](https://github.com/mehtabmahir/easy-whisper-ui), as it is user-friendly, has a GUI, and automates a lot of the hard stuff for you. |
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## List of Quants |
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Clicking on a link will download the corresponding quant instantly. |
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| Link | Quant | Size | Notes |
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|:-----|:-----|--------:|:------| |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-f32.bin) | F32 | 3.03 GB | Likely overkill. | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-f16.bin) | F16 | 1.52 GB | Performs better than Q8_0 for noisy audio and music. | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q8_0.bin) | Q8_0 | 818 MB | Sweet spot; superficial quality loss at nearly double the speed. | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q6_k.bin) | Q6_K | 637 MB | | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q5_k.bin) | Q5_K | 538 MB | | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q5_1.bin) | Q5_1 | 585 MB | | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q5_0.bin) | Q5_0 | 538 MB | Last "good" quant; anything below loses quality rapidly. | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q4_k.bin) | Q4_K | 444 MB | *Might* not have lost too much quality, but I'm not sure. | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q4_1.bin) | Q4_1 | 491 MB | | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q4_0.bin) | Q4_0 | 444 MB | | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q3_k.bin) | Q3_K | 345 MB | | |
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| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q2_k.bin) | Q2_K | 269 MB | Completely non-sensical output. | |
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The F16 quant was taken from [distil-whisper/distil-large-v3.5-ggml/ggml-model.bin](https://huggingface.co/distil-whisper/distil-large-v3.5-ggml/blob/main/ggml-model.bin). |
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## Questions you may have |
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### Why do the "K-quants" not work for me? |
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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. |
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### Are the K-quants "S", "M", or "L"? |
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The quantizer I was using was not specific about this, so I do not know about this either. |
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### What program did you use to make these quants? |
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I used [whisper.cpp v1.7.6](https://github.com/ggml-org/whisper.cpp/releases/tag/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. |
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### One or multiple of the quants are not working for me. |
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[Open a new discussion](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/discussions/new) in the community tab about this, and I will look into the issue. |
