---
base_model: HuggingFaceTB/smollm2-135M-SFT-Only
datasets:
- HuggingFaceTB/magpie-ultra-v1.0-top-300K-short-H4
- HuggingFaceTB/OpenHermes-2.5-H4-200k
- HuggingFaceTB/ifeval-like-data-36k-H4
- HuggingFaceTB/everyday-conversations-llama3.1-2k
- HuggingFaceTB/self-oss-instruct-sc2-H4
- HuggingFaceTB/summarization-data-10k-H4
- HuggingFaceTB/smollm-v2-summarization
- HuggingFaceTB/smollm-v2-rewriting-50k-H4
- HuggingFaceTB/explore-instruct-rewrite-H4
- HuggingFaceTB/LongAlign-16k-ctx-english-H4
language:
- en
library_name: transformers
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
---
## 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/HuggingFaceTB/smollm2-135M-SFT-Only

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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#smollm2-135M-SFT-Only-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/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.Q2_K.gguf) | Q2_K | 0.2 |  |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.Q3_K_S.gguf) | Q3_K_S | 0.2 |  |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.IQ4_XS.gguf) | IQ4_XS | 0.2 |  |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.Q3_K_L.gguf) | Q3_K_L | 0.2 |  |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.Q5_K_S.gguf) | Q5_K_S | 0.2 |  |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.Q5_K_M.gguf) | Q5_K_M | 0.2 |  |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/smollm2-135M-SFT-Only-GGUF/resolve/main/smollm2-135M-SFT-Only.f16.gguf) | f16 | 0.4 | 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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