---
arxiv: 2412.17743
base_model: yulan-team/YuLan-Mini
datasets:
- yulan-team/YuLan-Mini-Datasets
- HuggingFaceFW/fineweb-edu
- bigcode/the-stack-v2
- mlfoundations/dclm-baseline-1.0
- math-ai/AutoMathText
- gair-prox/open-web-math-pro
- RUC-AIBOX/long_form_thought_data_5k
- internlm/Lean-Workbook
- internlm/Lean-Github
- deepseek-ai/DeepSeek-Prover-V1
- ScalableMath/Lean-STaR-base
- ScalableMath/Lean-STaR-plus
- ScalableMath/Lean-CoT-base
- ScalableMath/Lean-CoT-plus
- opencsg/chinese-fineweb-edu
- liwu/MNBVC
- vikp/textbook_quality_programming
- HuggingFaceTB/smollm-corpus
- OpenCoder-LLM/opc-annealing-corpus
- OpenCoder-LLM/opc-sft-stage1
- OpenCoder-LLM/opc-sft-stage2
- XinyaoHu/AMPS_mathematica
- deepmind/math_dataset
- mrfakename/basic-math-10m
- microsoft/orca-math-word-problems-200k
- AI-MO/NuminaMath-CoT
- HuggingFaceTB/cosmopedia
- MU-NLPC/Calc-ape210k
- manu/project_gutenberg
- storytracer/LoC-PD-Books
- allenai/dolma
language:
- en
- zh
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- code
- math
---
## About

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static quants of https://huggingface.co/yulan-team/YuLan-Mini

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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/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.Q3_K_S.gguf) | Q3_K_S | 1.6 |  |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.Q2_K.gguf) | Q2_K | 1.6 |  |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.IQ4_XS.gguf) | IQ4_XS | 1.6 |  |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.Q3_K_L.gguf) | Q3_K_L | 1.7 |  |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.Q4_K_S.gguf) | Q4_K_S | 1.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.Q4_K_M.gguf) | Q4_K_M | 1.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.Q5_K_S.gguf) | Q5_K_S | 2.0 |  |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.Q5_K_M.gguf) | Q5_K_M | 2.1 |  |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.Q6_K.gguf) | Q6_K | 2.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.Q8_0.gguf) | Q8_0 | 2.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/YuLan-Mini-GGUF/resolve/main/YuLan-Mini.f16.gguf) | f16 | 5.0 | 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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