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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+MiniPLM-llama3.1-212M - AWQ
+- Model creator: https://huggingface.co/MiniLLM/
+- Original model: https://huggingface.co/MiniLLM/MiniPLM-llama3.1-212M/
+
+
+
+
+Original model description:
+---
+library_name: transformers
+license: apache-2.0
+datasets:
+- monology/pile-uncopyrighted
+- MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-generation
+---
+
+# MiniPLM-llama3.1-212M
+
+[paper](https://arxiv.org/abs/2410.17215) | [code](https://github.com/thu-coai/MiniPLM)
+
+**MiniPLM-llama3.1-212M** is a 212M model with the [LLaMA3.1 achitecture](https://arxiv.org/abs/2407.21783) pre-trained from scratch on [the Pile](https://huggingface.co/datasets/monology/pile-uncopyrighted) using the MiniPLM knowledge distillation framework with the [offcial Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) as the teacher model.
+This model shows the flexibility of the MiniPLM framework in conducting knowledge distillation across model families.
+
+We also open-source the [pre-training corpus](https://huggingface.co/datasets/MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5) refined by Difference Sampling in MiniPLM for reproducibility.
+
+<p align='left'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/2BqT0NgkmIXYlktovw9kG.png" width="1000">
+</p>
+
+## Evaluation
+
+MiniPLM models achieves better performance given the same computation and scales well across model sizes:
+
+<p align='left'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/EOYzajQcwQFT5PobqL3j0.png" width="1000">
+</p>
+
+## Baseline Models
++ [Conventional Pre-Training](https://huggingface.co/MiniLLM/Pretrain-LLama3.1-130M)
+
+## Citation
+
+```bibtex
+@article{miniplm,
+    title={MiniPLM: Knowledge Distillation for Pre-Training Language Models}, 
+    author={Yuxian Gu and Hao Zhou and Fandong Meng and Jie Zhou and Minlie Huang},
+    journal={arXiv preprint arXiv:2410.17215},
+    year={2024}
+}
+```
+