base_model: aisingapore/Gemma2-9b-WangchanLIONv2-instruct
datasets:
- airesearch/WangchanThaiInstruct
- airesearch/WangchanX-FLAN-v6.1
- airesearch/wangchanx-seed-free-synthetic-instruct-thai-120k
language:
- th
- en
library_name: transformers
license: gemma
quantized_by: mradermacher
About
static quants of https://huggingface.co/aisingapore/Gemma2-9b-WangchanLIONv2-instruct
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Gemma2-9b-WangchanLIONv2-instruct-i1-GGUF
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 | 3.9 | |
| GGUF | Q3_K_S | 4.4 | |
| GGUF | Q3_K_M | 4.9 | lower quality |
| GGUF | Q3_K_L | 5.2 | |
| GGUF | IQ4_XS | 5.3 | |
| GGUF | Q4_0_4_4 | 5.5 | fast on arm, low quality |
| GGUF | Q4_K_S | 5.6 | fast, recommended |
| GGUF | Q4_K_M | 5.9 | fast, recommended |
| GGUF | Q5_K_S | 6.6 | |
| GGUF | Q5_K_M | 6.7 | |
| GGUF | Q6_K | 7.7 | very good quality |
| GGUF | Q8_0 | 9.9 | fast, best quality |
| GGUF | f16 | 18.6 | 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. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.