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metadata
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

static quants of https://huggingface.co/lightblue/DeepSeek-R1-Distill-Qwen-14B-Multilingual

weighted/imatrix quants are available at https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-Multilingual-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs 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 Q2_K 5.9
GGUF Q3_K_S 6.8
GGUF Q3_K_M 7.4 lower quality
GGUF Q3_K_L 8.0
GGUF IQ4_XS 8.3
GGUF Q4_K_S 8.7 fast, recommended
GGUF Q4_K_M 9.1 fast, recommended
GGUF Q5_K_S 10.4
GGUF Q5_K_M 10.6
GGUF Q6_K 12.2 very good quality
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

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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.