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---
base_model: llmixer/BigWeave-v26-95b
language:
- en
library_name: transformers
license: unknown
quantized_by: mradermacher
tags:
- merge
- frankenmerge
- 95b
---
## About
<!-- ### quantize_version: 2 -->
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<!-- ### convert_type: hf -->
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<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/llmixer/BigWeave-v26-95b
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static quants are available at https://huggingface.co/mradermacher/BigWeave-v26-95b-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/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-IQ1_M.gguf) | i1-IQ1_M | 21.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 25.1 | |
| [GGUF](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 28.0 | |
| [GGUF](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-IQ2_M.gguf) | i1-IQ2_M | 31.9 | |
| [GGUF](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q2_K.gguf) | i1-Q2_K | 34.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 36.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 40.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-IQ3_M.gguf) | i1-IQ3_M | 42.4 | |
| [GGUF](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 45.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 49.7 | IQ3_M probably better |
| [PART 1](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 50.6 | |
| [PART 1](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 53.8 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 56.8 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 65.2 | |
| [PART 1](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigWeave-v26-95b-i1-GGUF/resolve/main/BigWeave-v26-95b.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 77.7 | practically like static Q6_K |
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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