Add info to readme

README.md

@@ -24,7 +24,8 @@ tags:
 - voice
 - distil
 ---
-
+# Distil-Large-v3.5 quants
+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). 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.
 ## List of Quants
 Clicking on a link will download the corresponding quant instantly.
 | Link | Quant | Size |
@@ -40,4 +41,11 @@ Clicking on a link will download the corresponding quant instantly.
 | [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 |
 | [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 |
 | [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 |
-| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q2_k.bin) | Q2_K (nonsensical output) | 269 MB |
+| [GGML](https://huggingface.co/Pomni/distil-large-v3.5-ggml-allquants/resolve/main/ggml-distil-large-v3.5-q2_k.bin) | Q2_K (nonsensical output) | 269 MB |
+## 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](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.