base_model: Neelectric/OLMo-2-1124-7B-Instruct_SFTv01.03
datasets: Neelectric/OpenR1-Math-cn_k12-86k
language:
- en
library_name: transformers
model_name: OLMo-2-1124-7B-Instruct_SFTv01.03
quantized_by: mradermacher
tags:
- generated_from_trainer
- open-r1
- trl
- sft
About
static quants of https://huggingface.co/Neelectric/OLMo-2-1124-7B-Instruct_SFTv01.03
weighted/imatrix quants are available at https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv01.03-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 | 3.0 | |
| GGUF | Q3_K_S | 3.4 | |
| GGUF | Q3_K_M | 3.8 | lower quality |
| GGUF | Q3_K_L | 4.1 | |
| GGUF | Q4_K_S | 4.3 | fast, recommended |
| GGUF | Q6_K | 6.1 | very good quality |
| GGUF | Q8_0 | 7.9 | 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
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.