auto-patch README.md
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README.md
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### quants_skip: -->
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<!-- ### skip_mmproj: -->
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static quants of https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-3b
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---
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base_model: CodeGoat24/UnifiedReward-2.0-qwen-3b
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datasets:
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- CodeGoat24/HPD
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- CodeGoat24/LiFT-HRA
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- CodeGoat24/OIP
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- CodeGoat24/EvalMuse
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- CodeGoat24/ShareGPTVideo-DPO
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- CodeGoat24/VideoFeedback
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- CodeGoat24/LLaVA-Critic-113k
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- CodeGoat24/VideoDPO
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language:
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- en
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library_name: transformers
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license: mit
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mradermacher:
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readme_rev: 1
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quantized_by: mradermacher
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---
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## About
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### quants_skip: -->
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<!-- ### skip_mmproj: -->
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static quants of https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-3b
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<!-- provided-files -->
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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#UnifiedReward-2.0-qwen-3b-GGUF).***
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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.
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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more details, including on how to concatenate multi-part files.
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## Provided Quants
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.9 | multi-modal supplement |
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| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.mmproj-f16.gguf) | mmproj-f16 | 1.4 | multi-modal supplement |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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See https://huggingface.co/mradermacher/model_requests for some answers to
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questions you might have and/or if you want some other model quantized.
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## Thanks
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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me use its servers and providing upgrades to my workstation to enable
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this work in my free time.
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<!-- end -->
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