base_model: 01-ai/yi-34b-200k
datasets:
- jondurbin/airoboros-3.2
- bluemoon-fandom-1-1-rp-cleaned
- boolq
- jondurbin/gutenberg-dpo-v0.1
- LDJnr/Capybara
- jondurbin/cinematika-v0.1
- glaiveai/glaive-function-calling-v2
- grimulkan/LimaRP-augmented
- piqa
- Vezora/Tested-22k-Python-Alpaca
- mattpscott/airoboros-summarization
- unalignment/toxic-dpo-v0.2
exported_from: jondurbin/airoboros-34b-3.3
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
license_name: yi-license
quantized_by: mradermacher
About
static quants of https://huggingface.co/jondurbin/airoboros-34b-3.3
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 | 13.5 | |
| GGUF | Q3_K_S | 15.6 | |
| GGUF | IQ3_S | 15.7 | beats Q3_K* |
| GGUF | Q3_K_M | 17.3 | lower quality |
| GGUF | Q3_K_L | 18.8 | |
| GGUF | Q4_K_S | 20.2 | fast, recommended |
| GGUF | Q4_K_M | 21.3 | fast, recommended |
| GGUF | Q5_K_S | 24.3 | |
| GGUF | Q5_K_M | 25.0 | |
| GGUF | Q6_K | 28.9 | very good quality |
| GGUF | Q8_0 | 37.1 | fast, best quality |
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
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.