README.md

---
base_model:
- 152334H/miqu-1-70b-sf
- lizpreciatior/lzlv_70b_fp16_hf
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About

weighted/imatrix quants of https://huggingface.co/wolfram/miquliz-120b-v2.0
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## 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/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ1_S.gguf) | i1-IQ1_S | 25.6 |  |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 32.1 |  |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 35.7 |  |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q2_K.gguf) | i1-Q2_K | 44.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 47.2 | fast, lower quality |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_XS.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_XS.gguf.split-ab) | i1-Q3_K_XS | 49.2 |  |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_S.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_S.gguf.split-ab) | i1-Q3_K_S | 52.1 | IQ3_XS probably better |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_M.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_M.gguf.split-ab) | i1-Q3_K_M | 58.1 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_L.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_L.gguf.split-ab) | i1-Q3_K_L | 63.3 | IQ3_M probably better |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q4_K_S.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q4_K_S.gguf.split-ab) | i1-Q4_K_S | 68.6 | almost as good as Q4_K_M |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q4_K_M.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q4_K_M.gguf.split-ab) | i1-Q4_K_M | 72.5 | fast, medium quality |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 85.3 | best weighted quant |


Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

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