base_model: lordjia/Qwen2-Cantonese-7B-Instruct
datasets:
- jed351/cantonese-wikipedia
- raptorkwok/cantonese-traditional-chinese-parallel-corpus
language:
- zh
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- Cantonese
- Qwen2
- chat
About
static quants of https://huggingface.co/lordjia/Qwen2-Cantonese-7B-Instruct
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q2_K | 3.1 | |
| GGUF | IQ3_XS | 3.4 | |
| GGUF | Q3_K_S | 3.6 | |
| GGUF | IQ3_S | 3.6 | beats Q3_K* |
| GGUF | IQ3_M | 3.7 | |
| GGUF | Q3_K_M | 3.9 | lower quality |
| GGUF | Q3_K_L | 4.2 | |
| GGUF | IQ4_XS | 4.4 | |
| GGUF | Q4_K_S | 4.6 | fast, recommended |
| GGUF | Q4_K_M | 4.8 | fast, recommended |
| GGUF | Q5_K_S | 5.4 | |
| GGUF | Q5_K_M | 5.5 | |
| GGUF | Q6_K | 6.4 | very good quality |
| GGUF | Q8_0 | 8.2 | fast, best quality |
| GGUF | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.