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Model: mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF Source: Original Platform
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---
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base_model: TMLR-Group-HF/Co-rewarding-II-Qwen2.5-7B-MATH
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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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<!-- ### vocab_type: -->
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<!-- ### tags: nicoboss -->
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<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
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<!-- ### quants_skip: -->
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<!-- ### skip_mmproj: -->
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weighted/imatrix quants of https://huggingface.co/TMLR-Group-HF/Co-rewarding-II-Qwen2.5-7B-MATH
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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#Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF).***
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static quants are available at https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-GGUF
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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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||||
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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/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
|
||||
| [GGUF](https://huggingface.co/mradermacher/Co-rewarding-II-Qwen2.5-7B-MATH-i1-GGUF/resolve/main/Co-rewarding-II-Qwen2.5-7B-MATH.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
|
||||
|
||||
Here is a handy graph by ikawrakow comparing some lower-quality quant
|
||||
types (lower is better):
|
||||
|
||||

|
||||
|
||||
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 -->
|
||||
Reference in New Issue
Block a user