---
base_model: LTS-VVE/Teuta
datasets:
- LTS-VVE/Teuta-sq
- LTS-VVE/grammar_sq_0.1
- LTS-VVE/linguistic_sq
- LTS-VVE/Math-physics-dataset-sq
- LTS-VVE/albanian-synthetic
- noxneural/lilium_albanicum_eng_alb
- MIND-Lab/Safety-Evaluation
- shb777/simple-math-steps-7M
- RishiKompelli/TherapyDataset
- microsoft/orca-math-word-problems-200k
- Vezora/Tested-143k-Python-Alpaca
- AI4Chem/ChemPref-DPO-for-Chemistry-data-en
- jkhedri/psychology-dataset
- samhog/psychology-10k
- Amod/mental_health_counseling_conversations
- sayhan/strix-philosophy-qa
- Maverfrick/Rust_dataset
- Neloy262/rust_instruction_dataset
- Tesslate/Rust_Dataset
language:
- en
- sq
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
- al
- math
- philosophy
- chemistry
- code
- biology
- climate
- not-for-all-audiences
---
## About

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static quants of https://huggingface.co/LTS-VVE/Teuta

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

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Teuta-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/Teuta-GGUF/resolve/main/Teuta.Q2_K.gguf) | Q2_K | 1.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.Q3_K_S.gguf) | Q3_K_S | 1.6 |  |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.Q3_K_L.gguf) | Q3_K_L | 1.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.IQ4_XS.gguf) | IQ4_XS | 1.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.Q4_K_S.gguf) | Q4_K_S | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.Q4_K_M.gguf) | Q4_K_M | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.Q5_K_S.gguf) | Q5_K_S | 2.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.Q5_K_M.gguf) | Q5_K_M | 2.4 |  |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.Q6_K.gguf) | Q6_K | 2.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.Q8_0.gguf) | Q8_0 | 3.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Teuta-GGUF/resolve/main/Teuta.f16.gguf) | f16 | 6.5 | 16 bpw, overkill |

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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