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---
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# Mistral AI Research License
If You want to use a Mistral Model, a Derivative or an Output for any purpose that is not expressly authorized under this Agreement, You must request a license from Mistral AI, which Mistral AI may grant to You in Mistral AI's sole discretion. To discuss such a license, please contact Mistral AI via the website contact form: https://mistral.ai/contact/
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## 9. Definitions
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language:
- en
- fr
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- es
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library_name: transformers
license: other
license_link: https://mistral.ai/licenses/MRL-0.1.md
license_name: mrl
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/mistralai/Mistral-Large-Instruct-2411
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q2_K.gguf) | Q2_K | 45.3 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q3_K_S.gguf.part2of2) | Q3_K_S | 52.9 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q3_K_M.gguf.part2of2) | Q3_K_M | 59.2 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q3_K_L.gguf.part2of2) | Q3_K_L | 64.7 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.IQ4_XS.gguf.part2of2) | IQ4_XS | 66.1 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q4_K_S.gguf.part2of2) | Q4_K_S | 69.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q4_K_M.gguf.part2of2) | Q4_K_M | 73.3 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q5_K_S.gguf.part2of2) | Q5_K_S | 84.5 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q5_K_M.gguf.part2of2) | Q5_K_M | 86.6 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q6_K.gguf.part3of3) | Q6_K | 100.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Mistral-Large-Instruct-2411-GGUF/resolve/main/Mistral-Large-Instruct-2411.Q8_0.gguf.part3of3) | Q8_0 | 130.4 | 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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/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.
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