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Phi-2-OmniMatrix-GGUF
mradermacher/Phi-2-OmniMatrix-GGUF
Phi-2-OmniMatrix-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.
static quants of https://huggingface.co/bunnycore/Phi-2-OmniMatrix
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.gguf28.8 GB · 100%
At a glance
- Library
- transformers
- Access
- Public
- Created
- Sep 14, 2024
- Updated
- Sep 15, 2024
- SHA
- 22873506
Base models
Model card
From the Hugging Face model README
About
<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: -->static quants of https://huggingface.co/bunnycore/Phi-2-OmniMatrix
<!-- provided-files -->weighted/imatrix quants are available at https://huggingface.co/mradermacher/Phi-2-OmniMatrix-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 | 1.2 | |
| GGUF | IQ3_XS | 1.3 | |
| GGUF | IQ3_S | 1.4 | beats Q3_K* |
| GGUF | Q3_K_S | 1.4 | |
| GGUF | IQ3_M | 1.4 | |
| GGUF | Q3_K_M | 1.5 | lower quality |
| GGUF | IQ4_XS | 1.6 | |
| GGUF | Q3_K_L | 1.7 | |
| GGUF | Q4_K_S | 1.7 | fast, recommended |
| GGUF | Q4_K_M | 1.8 | fast, recommended |
| GGUF | Q5_K_S | 2.0 | |
| GGUF | Q5_K_M | 2.1 | |
| GGUF | Q6_K | 2.4 | very good quality |
| GGUF | Q8_0 | 3.1 | fast, best quality |
| GGUF | f16 | 5.7 | 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.
<!-- end -->- Library
- transformers
- Languages
- en
- Created
- Sep 14, 2024
- Updated
- Sep 15, 2024