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oscar-10b-GGUF
mradermacher/oscar-10b-GGUF
oscar-10b-GGUF is a machine learning model from mradermacher. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
static quants of https://huggingface.co/sydonayrex/oscar-10b
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At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Nov 21, 2024
- Updated
- Nov 21, 2024
- SHA
- 42d02b25
Base models
Model card
From the Hugging Face model README
About
<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss -->static quants of https://huggingface.co/sydonayrex/oscar-10b
<!-- provided-files -->weighted/imatrix quants are available at https://huggingface.co/mradermacher/oscar-10b-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 | 4.0 | |
| GGUF | Q3_K_S | 4.7 | |
| GGUF | Q3_K_M | 5.2 | lower quality |
| GGUF | Q3_K_L | 5.6 | |
| GGUF | IQ4_XS | 5.7 | |
| GGUF | Q4_0_4_4 | 6.0 | fast on arm, low quality |
| GGUF | Q4_K_S | 6.0 | fast, recommended |
| GGUF | Q4_K_M | 6.3 | fast, recommended |
| GGUF | Q5_K_S | 7.2 | |
| GGUF | Q5_K_M | 7.4 | |
| GGUF | Q6_K | 8.5 | very good quality |
| GGUF | Q8_0 | 11.0 | fast, best quality |
| GGUF | f16 | 20.6 | 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. 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.
<!-- end -->- Library
- transformers
- License
- apache-2.0
- Languages
- en
- Created
- Nov 21, 2024
- Updated
- Nov 21, 2024