---
base_model: TildeAI/TildeOpen-30b
datasets:
- HPLT/HPLT2.0_cleaned
- HPLT/hplt_monolingual_v1_2
- HuggingFaceFW/fineweb-2
- allenai/MADLAD-400
- uonlp/CulturaX
- bigcode/the-stack
- common-pile/arxiv_papers
language:
- en
- de
- fr
- pl
- ru
- it
- pt
- cs
- nl
- es
- fi
- tr
- hu
- bg
- uk
- bs
- hr
- da
- et
- lt
- ro
- sk
- sl
- sv
- no
- lv
- sr
- sq
- mk
- is
- mt
- ga
library_name: transformers
license: cc-by-4.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
---
## About

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<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
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static quants of https://huggingface.co/TildeAI/TildeOpen-30b

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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#TildeOpen-30b-GGUF).***

weighted/imatrix quants are available at https://huggingface.co/mradermacher/TildeOpen-30b-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/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.Q2_K.gguf) | Q2_K | 11.7 |  |
| [GGUF](https://huggingface.co/mradermacher/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.Q3_K_S.gguf) | Q3_K_S | 13.6 |  |
| [GGUF](https://huggingface.co/mradermacher/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.Q3_K_M.gguf) | Q3_K_M | 15.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.Q3_K_L.gguf) | Q3_K_L | 16.3 |  |
| [GGUF](https://huggingface.co/mradermacher/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.IQ4_XS.gguf) | IQ4_XS | 16.8 |  |
| [GGUF](https://huggingface.co/mradermacher/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.Q4_K_S.gguf) | Q4_K_S | 17.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.Q4_K_M.gguf) | Q4_K_M | 18.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.Q5_K_S.gguf) | Q5_K_S | 21.3 |  |
| [GGUF](https://huggingface.co/mradermacher/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.Q5_K_M.gguf) | Q5_K_M | 21.9 |  |
| [GGUF](https://huggingface.co/mradermacher/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.Q6_K.gguf) | Q6_K | 25.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TildeOpen-30b-GGUF/resolve/main/TildeOpen-30b.Q8_0.gguf) | Q8_0 | 32.7 | fast, best quality |

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

## 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.

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