---
base_model: NiuTrans/LMT-60-4B
datasets:
- NiuTrans/LMT-60-sft-data
language:
- en
- zh
- ar
- es
- de
- fr
- it
- ja
- nl
- pl
- pt
- ru
- tr
- bg
- bn
- cs
- da
- el
- fa
- fi
- hi
- hu
- id
- ko
- nb
- ro
- sk
- sv
- th
- uk
- vi
- am
- az
- bo
- he
- hr
- hy
- is
- jv
- ka
- kk
- km
- ky
- lo
- mn
- mr
- ms
- my
- ne
- ps
- si
- sw
- ta
- te
- tg
- tl
- ug
- ur
- uz
- yue
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
---
## About

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<!-- ### quants:  x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
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static quants of https://huggingface.co/NiuTrans/LMT-60-4B

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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#LMT-60-4B-GGUF).***

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/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.Q2_K.gguf) | Q2_K | 1.9 |  |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.Q3_K_S.gguf) | Q3_K_S | 2.2 |  |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.Q3_K_M.gguf) | Q3_K_M | 2.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.Q3_K_L.gguf) | Q3_K_L | 2.5 |  |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.IQ4_XS.gguf) | IQ4_XS | 2.6 |  |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.Q4_K_S.gguf) | Q4_K_S | 2.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.Q4_K_M.gguf) | Q4_K_M | 2.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.Q5_K_S.gguf) | Q5_K_S | 3.2 |  |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.Q5_K_M.gguf) | Q5_K_M | 3.3 |  |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.Q6_K.gguf) | Q6_K | 3.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.Q8_0.gguf) | Q8_0 | 4.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/LMT-60-4B-GGUF/resolve/main/LMT-60-4B.f16.gguf) | f16 | 8.9 | 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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