README.md

---
base_model: lightblue/DeepSeek-R1-Distill-Qwen-14B-Multilingual
datasets:
- lightblue/reasoning-multilingual-R1-Llama-70B-train
language:
- am
- ar
- bn
- zh
- cs
- nl
- en
- fr
- de
- el
- ha
- he
- hi
- id
- it
- ja
- jv
- km
- ko
- lo
- ms
- mr
- fa
- pl
- pt
- ro
- ru
- es
- sw
- sv
- tl
- ta
- te
- th
- tr
- uk
- ur
- vi
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- reasoning
---
## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags:  -->
static quants of https://huggingface.co/lightblue/DeepSeek-R1-Distill-Qwen-14B-Multilingual

<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.Q2_K.gguf) | Q2_K | 5.9 |  |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.Q3_K_S.gguf) | Q3_K_S | 6.8 |  |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.Q3_K_L.gguf) | Q3_K_L | 8.0 |  |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.IQ4_XS.gguf) | IQ4_XS | 8.3 |  |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.Q5_K_S.gguf) | Q5_K_S | 10.4 |  |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.Q5_K_M.gguf) | Q5_K_M | 10.6 |  |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-14B-Multilingual.Q8_0.gguf) | Q8_0 | 15.8 | 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.

<!-- end -->