---
base_model: Edentns/DataVortexTL-1.1B-v0.1
datasets:
- beomi/KoAlpaca-v1.1a
- jojo0217/korean_rlhf_dataset
- kyujinpy/OpenOrca-KO
- nlpai-lab/kullm-v2
language:
- ko
library_name: transformers
license: cc-by-nc-sa-4.0
quantized_by: mradermacher
tags:
- text-generation
---
## About

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static quants of https://huggingface.co./Edentns/DataVortexTL-1.1B-v0.1

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weighted/imatrix quants are available at https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-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/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q2_K.gguf) | Q2_K | 0.5 |  |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q3_K_S.gguf) | Q3_K_S | 0.6 |  |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q3_K_M.gguf) | Q3_K_M | 0.6 | lower quality |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q3_K_L.gguf) | Q3_K_L | 0.7 |  |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.IQ4_XS.gguf) | IQ4_XS | 0.7 |  |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q4_0_4_4.gguf) | Q4_0_4_4 | 0.7 | fast on arm, low quality |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q4_K_S.gguf) | Q4_K_S | 0.7 | fast, recommended |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q5_K_S.gguf) | Q5_K_S | 0.9 |  |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q5_K_M.gguf) | Q5_K_M | 0.9 |  |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q6_K.gguf) | Q6_K | 1.0 | very good quality |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.Q8_0.gguf) | Q8_0 | 1.3 | fast, best quality |
| [GGUF](https://huggingface.co./mradermacher/DataVortexTL-1.1B-v0.1-GGUF/resolve/main/DataVortexTL-1.1B-v0.1.f16.gguf) | f16 | 2.3 | 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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