|
--- |
|
base_model: Edentns/DataVortexS-10.7B-dpo-v1.6 |
|
language: |
|
- ko |
|
library_name: transformers |
|
license: cc-by-nc-sa-4.0 |
|
quantized_by: mradermacher |
|
tags: |
|
- text-generation |
|
--- |
|
## About |
|
|
|
<!-- ### quantize_version: 2 --> |
|
<!-- ### output_tensor_quantised: 1 --> |
|
<!-- ### convert_type: hf --> |
|
<!-- ### vocab_type: --> |
|
<!-- ### tags: --> |
|
static quants of https://huggingface.co/Edentns/DataVortexS-10.7B-dpo-v1.6 |
|
|
|
<!-- provided-files --> |
|
weighted/imatrix quants are available at https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-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/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q2_K.gguf) | Q2_K | 4.2 | | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q3_K_S.gguf) | Q3_K_S | 4.8 | | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q3_K_M.gguf) | Q3_K_M | 5.4 | lower quality | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q3_K_L.gguf) | Q3_K_L | 5.8 | | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.IQ4_XS.gguf) | IQ4_XS | 6.0 | | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q4_0_4_4.gguf) | Q4_0_4_4 | 6.3 | fast on arm, low quality | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q4_K_S.gguf) | Q4_K_S | 6.3 | fast, recommended | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q4_K_M.gguf) | Q4_K_M | 6.7 | fast, recommended | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q5_K_S.gguf) | Q5_K_S | 7.6 | | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q5_K_M.gguf) | Q5_K_M | 7.8 | | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q6_K.gguf) | Q6_K | 9.0 | very good quality | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.Q8_0.gguf) | Q8_0 | 11.6 | fast, best quality | |
|
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.6-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.6.f16.gguf) | f16 | 21.8 | 16 bpw, overkill | |
|
|
|
Here is a handy graph by ikawrakow comparing some lower-quality quant |
|
types (lower is better): |
|
|
|
 |
|
|
|
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. |
|
|
|
<!-- end --> |
|
|
