File size: 7,613 Bytes
2810dd8 d4912c0 2810dd8 a50b0eb 2810dd8 f071213 2810dd8 f071213 2810dd8 f071213 2810dd8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
---
base_model: leafspark/Mistral-Large-218B-Instruct
language:
- en
- fr
- de
- es
- it
- pt
- zh
- ja
- ru
- ko
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: -->
static quants of https://huggingface.co/leafspark/Mistral-Large-218B-Instruct
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-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 |
|:-----|:-----|--------:|:------|
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q2_K.gguf.part2of2) | Q2_K | 80.4 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q3_K_S.gguf.part2of2) | Q3_K_S | 94.0 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.IQ3_S.gguf.part2of2) | IQ3_S | 94.3 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q3_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q3_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q3_K_M.gguf.part3of3) | Q3_K_M | 105.2 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q3_K_L.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q3_K_L.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q3_K_L.gguf.part3of3) | Q3_K_L | 114.9 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.IQ4_XS.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.IQ4_XS.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.IQ4_XS.gguf.part3of3) | IQ4_XS | 117.5 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q4_K_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q4_K_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q4_K_S.gguf.part3of3) | Q4_K_S | 123.8 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q4_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q4_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q4_K_M.gguf.part3of3) | Q4_K_M | 130.2 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q5_K_S.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q5_K_S.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q5_K_S.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q5_K_S.gguf.part4of4) | Q5_K_S | 150.1 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q5_K_M.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q5_K_M.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q5_K_M.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q5_K_M.gguf.part4of4) | Q5_K_M | 153.9 | |
| [PART 1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q6_K.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q6_K.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q6_K.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q6_K.gguf.part4of4) | Q6_K | 179.0 | very good quality |
| [P1](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q8_0.gguf.part1of5) [P2](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q8_0.gguf.part2of5) [P3](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q8_0.gguf.part3of5) [P4](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q8_0.gguf.part4of5) [P5](https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-GGUF/resolve/main/Mistral-Large-218B-Instruct.Q8_0.gguf.part5of5) | Q8_0 | 231.9 | 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](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.
<!-- end -->
|