base_model: rinna/llama-3-youko-8b-instruct
datasets:
- CohereForAI/aya_dataset
- kunishou/databricks-dolly-15k-ja
- kunishou/HelpSteer-35k-ja
- kunishou/HelpSteer2-20k-ja
- kunishou/hh-rlhf-49k-ja
- kunishou/oasst1-chat-44k-ja
- kunishou/oasst2-chat-68k-ja
- meta-math/MetaMathQA
- OpenAssistant/oasst1
- OpenAssistant/oasst2
- sahil2801/CodeAlpaca-20k
language:
- ja
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
tags:
- llama
- llama-3
About
static quants of https://huggingface.co/rinna/llama-3-youko-8b-instruct
weighted/imatrix quants are available at https://huggingface.co/mradermacher/llama-3-youko-8b-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 | 3.3 | |
| GGUF | IQ3_XS | 3.6 | |
| GGUF | Q3_K_S | 3.8 | |
| GGUF | IQ3_S | 3.8 | beats Q3_K* |
| GGUF | IQ3_M | 3.9 | |
| GGUF | Q3_K_M | 4.1 | lower quality |
| GGUF | Q3_K_L | 4.4 | |
| GGUF | IQ4_XS | 4.6 | |
| GGUF | Q4_K_S | 4.8 | fast, recommended |
| GGUF | Q4_K_M | 5.0 | fast, recommended |
| GGUF | Q5_K_S | 5.7 | |
| GGUF | Q5_K_M | 5.8 | |
| GGUF | Q6_K | 6.7 | very good quality |
| GGUF | Q8_0 | 8.6 | fast, best quality |
| GGUF | f16 | 16.2 | 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.