base_model: Weyaxi/Newton-7B
datasets:
- hendrycks/competition_math
- allenai/ai2_arc
- camel-ai/physics
- camel-ai/chemistry
- camel-ai/biology
- camel-ai/math
- STEM-AI-mtl/Electrical-engineering
- openbookqa
- piqa
- metaeval/reclor
- mandyyyyii/scibench
- derek-thomas/ScienceQA
- sciq
- TIGER-Lab/ScienceEval
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- axolotl
- finetune
- qlora
About
static quants of https://huggingface.co/Weyaxi/Newton-7B
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Newton-7B-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 | 2.8 | |
| GGUF | Q3_K_S | 3.3 | |
| GGUF | Q3_K_M | 3.6 | lower quality |
| GGUF | Q3_K_L | 3.9 | |
| GGUF | IQ4_XS | 4.0 | |
| GGUF | Q4_0_4_4 | 4.2 | fast on arm, low quality |
| GGUF | Q4_K_S | 4.2 | fast, recommended |
| GGUF | Q4_K_M | 4.5 | fast, recommended |
| GGUF | Q5_K_S | 5.1 | |
| GGUF | Q5_K_M | 5.2 | |
| GGUF | Q6_K | 6.0 | very good quality |
| GGUF | Q8_0 | 7.8 | fast, best quality |
| GGUF | f16 | 14.6 | 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.