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RichardErkhov/gair-prox_-_math-chunk-refining-lm-gguf
gair-prox_-_math-chunk-refining-lm-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
285
5% of all-time downloads
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.gguf4.8 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
math-chunk-refining-lm - GGUF
| Name | Quant method | Size |
|---|---|---|
| math-chunk-refining-lm.Q2_K.gguf | Q2_K | 0.14GB |
| math-chunk-refining-lm.IQ3_XS.gguf | IQ3_XS | 0.15GB |
| math-chunk-refining-lm.IQ3_S.gguf | IQ3_S | 0.16GB |
| math-chunk-refining-lm.Q3_K_S.gguf | Q3_K_S | 0.16GB |
| math-chunk-refining-lm.IQ3_M.gguf | IQ3_M | 0.16GB |
| math-chunk-refining-lm.Q3_K.gguf | Q3_K | 0.17GB |
| math-chunk-refining-lm.Q3_K_M.gguf | Q3_K_M | 0.17GB |
| math-chunk-refining-lm.Q3_K_L.gguf | Q3_K_L | 0.18GB |
| math-chunk-refining-lm.IQ4_XS.gguf | IQ4_XS | 0.19GB |
| math-chunk-refining-lm.Q4_0.gguf | Q4_0 | 0.2GB |
| math-chunk-refining-lm.IQ4_NL.gguf | IQ4_NL | 0.2GB |
| math-chunk-refining-lm.Q4_K_S.gguf | Q4_K_S | 0.2GB |
| math-chunk-refining-lm.Q4_K.gguf | Q4_K | 0.2GB |
| math-chunk-refining-lm.Q4_K_M.gguf | Q4_K_M | 0.2GB |
| math-chunk-refining-lm.Q4_1.gguf | Q4_1 | 0.21GB |
| math-chunk-refining-lm.Q5_0.gguf | Q5_0 | 0.23GB |
| math-chunk-refining-lm.Q5_K_S.gguf | Q5_K_S | 0.23GB |
| math-chunk-refining-lm.Q5_K.gguf | Q5_K | 0.24GB |
| math-chunk-refining-lm.Q5_K_M.gguf | Q5_K_M | 0.24GB |
| math-chunk-refining-lm.Q5_1.gguf | Q5_1 | 0.25GB |
| math-chunk-refining-lm.Q6_K.gguf | Q6_K | 0.27GB |
| math-chunk-refining-lm.Q8_0.gguf | Q8_0 | 0.35GB |
license: apache-2.0 datasets:
Math-chunk-refining-lm is an adapted 0.3B-ProX model, fine-tuned for doc level refining via program generation, and can be applied over math pre-training corpus such as open-web-math.
<p align="center"> <img src="func_design.png"> </p>@article{zhou2024programming,
title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
journal={arXiv preprint arXiv:2409.17115},
year={2024}
}