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Model: mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF
Source: Original Platform

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README.md

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+---
+base_model: HuggingFaceTB/SmolVLM2-2.2B-Instruct
+datasets:
+- HuggingFaceM4/the_cauldron
+- HuggingFaceM4/Docmatix
+- lmms-lab/LLaVA-OneVision-Data
+- lmms-lab/M4-Instruct-Data
+- HuggingFaceFV/finevideo
+- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
+- lmms-lab/LLaVA-Video-178K
+- orrzohar/Video-STaR
+- Mutonix/Vript
+- TIGER-Lab/VISTA-400K
+- Enxin/MovieChat-1K_train
+- ShareGPT4Video/ShareGPT4Video
+language:
+- en
+library_name: transformers
+license: apache-2.0
+mradermacher:
+  readme_rev: 1
+quantized_by: mradermacher
+tags:
+- video-text-to-text
+---
+## About
+
+<!-- ### quantize_version: 2 -->
+<!-- ### output_tensor_quantised: 1 -->
+<!-- ### convert_type: hf -->
+<!-- ### vocab_type:  -->
+<!-- ### tags: nicoboss -->
+weighted/imatrix quants of https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct
+
+<!-- provided-files -->
+
+***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#SmolVLM2-2.2B-Instruct-i1-GGUF).***
+
+static quants are available at https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-GGUF
+
+**This is a vision model - mmproj files (if any) will be in the [static repository](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-GGUF).**
+## 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/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 0.5 | for the desperate |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.6 |  |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 |  |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 0.7 |  |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 0.8 |  |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.8 | very low quality |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 0.8 | IQ3_XXS probably better |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.8 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.9 |  |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 0.9 | beats Q3_K* |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.9 | IQ3_XS probably better |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 1.0 |  |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.0 | IQ3_S probably better |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.1 | IQ3_M probably better |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.1 | prefer IQ4_XS |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 1.2 | fast, low quality |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.2 | optimal size/speed/quality |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.2 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q4_1.gguf) | i1-Q4_1 | 1.3 |  |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/SmolVLM2-2.2B-Instruct-i1-GGUF/resolve/main/SmolVLM2-2.2B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 1.6 | practically like static Q6_K |
+
+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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
+
+<!-- end -->

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