base_model: rinna/nekomata-7b-instruction
datasets:
- databricks/databricks-dolly-15k
- kunishou/databricks-dolly-15k-ja
- izumi-lab/llm-japanese-dataset
language:
- ja
- en
library_name: transformers
license: other
license_link: >-
https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT
license_name: tongyi-qianwen-license-agreement
quantized_by: mradermacher
tags:
- qwen
About
static quants of https://huggingface.co/rinna/nekomata-7b-instruction
weighted/imatrix quants are available at https://huggingface.co/mradermacher/nekomata-7b-instruction-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 | 3.1 | |
| GGUF | IQ3_XS | 3.6 | |
| GGUF | IQ3_S | 3.7 | beats Q3_K* |
| GGUF | Q3_K_S | 3.7 | |
| GGUF | IQ3_M | 4.0 | |
| GGUF | Q3_K_M | 4.2 | lower quality |
| GGUF | Q3_K_L | 4.4 | |
| GGUF | IQ4_XS | 4.4 | |
| GGUF | Q4_K_S | 4.6 | fast, recommended |
| GGUF | Q4_K_M | 5.0 | fast, recommended |
| GGUF | Q5_K_S | 5.5 | |
| GGUF | Q5_K_M | 5.8 | |
| GGUF | Q6_K | 6.4 | very good quality |
| GGUF | Q8_0 | 8.3 | fast, best quality |
| GGUF | f16 | 15.5 | 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.