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---
base_model:
- 152334H/miqu-1-70b-sf
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
static quants of https://huggingface.co/wolfram/miqu-1-103b
weighted/imatrix quants available at https://huggingface.co/mradermacher/miqu-1-103b-i1-GGUF
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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/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q2_K.gguf) | Q2_K | 38.2 | |
| [GGUF](https://huggingface.co/mradermacher/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q3_K_S.gguf) | Q3_K_S | 44.8 | |
| [PART 1](https://huggingface.co/mradermacher/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q3_K_M.gguf.part2of2) | Q3_K_M | 49.9 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q4_K_M.gguf.part2of2) | Q4_K_M | 62.2 | fast, medium quality |
| [PART 1](https://huggingface.co/mradermacher/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q6_K.gguf.part2of2) | Q6_K | 85.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/miqu-1-103b-GGUF/resolve/main/miqu-1-103b.Q8_0.gguf.part3of3) | Q8_0 | 109.9 | fast, best quality |
Here is a handy graph comparing some lower-quality quant types (lower is better):
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