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LLaMA2-13B-RFT-GGUF
mradermacher/LLaMA2-13B-RFT-GGUF
LLaMA2-13B-RFT-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/GraphWiz/LLaMA2-13B-RFT
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At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Nov 26, 2024
- Updated
- Nov 28, 2024
- SHA
- f580458f
Base models
Datasets
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/GraphWiz/LLaMA2-13B-RFT
<!-- provided-files -->weighted/imatrix quants are available at https://huggingface.co/mradermacher/LLaMA2-13B-RFT-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 | 5.0 | |
| GGUF | Q3_K_S | 5.8 | |
| GGUF | Q3_K_M | 6.4 | lower quality |
| GGUF | Q3_K_L | 7.0 | |
| GGUF | IQ4_XS | 7.1 | |
| GGUF | Q4_0_4_4 | 7.5 | fast on arm, low quality |
| GGUF | Q4_K_S | 7.5 | fast, recommended |
| GGUF | Q4_K_M | 8.0 | fast, recommended |
| GGUF | Q5_K_S | 9.1 | |
| GGUF | Q5_K_M | 9.3 | |
| GGUF | Q6_K | 10.8 | very good quality |
| GGUF | Q8_0 | 13.9 | 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.
<!-- end -->- Library
- transformers
- License
- apache-2.0
- Languages
- en
- Created
- Nov 26, 2024
- Updated
- Nov 28, 2024