MiniLLM_-_Pretrain-Qwen-500M-exl2

Quantization made by Richard Erkhov.

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Pretrain-Qwen-500M - EXL2

• Model creator: https://huggingface.co/MiniLLM/
• Original model: https://huggingface.co/MiniLLM/Pretrain-Qwen-500M/

Available sizes

| Branch | Bits | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ | | 8_0 | 8.0 | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | 6_5 | 6.5 | Very similar to 8.0, good tradeoff of size vs performance, recommended. | | 5_0 | 5.0 | Slightly lower quality vs 6.5, but usable on 8GB cards. | | 4_25 | 4.25 | GPTQ equivalent bits per weight, slightly higher quality. | | 3_5 | 3.5 | Lower quality, only use if you have to. |

Download instructions

With git:

git clone --single-branch --branch 6_5 https://huggingface.co/MiniLLM_-_Pretrain-Qwen-500M-exl2 Pretrain-Qwen-500M-6_5

With huggingface hub:

pip3 install huggingface-hub

To download a specific branch, use the --revision parameter. For example, to download the 6.5 bpw branch: Linux:

huggingface-cli download MiniLLM_-_Pretrain-Qwen-500M-exl2 --revision 6_5 --local-dir Pretrain-Qwen-500M-6_5 --local-dir-use-symlinks False

Windows (which apparently doesn't like _ in folders sometimes?):

huggingface-cli download MiniLLM_-_Pretrain-Qwen-500M-exl2 --revision 6_5 --local-dir Pretrain-Qwen-500M-6.5 --local-dir-use-symlinks False

Original model description:

library_name: transformers license: apache-2.0 datasets:

• monology/pile-uncopyrighted
• MiniLLM/pile-tokenized language:
• en metrics:
• accuracy pipeline_tag: text-generation

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Pretrain-Qwen-500M

paper | code

Pretrain-Qwen-500M is a 500M model with Qwen achitecture conventionally pre-trained from scratch on the Pile for 50B tokens.

We also open-source the tokenized pre-training corpus for reproducibility.

It is used as the baseline for MiniLLM-Qwen-500M

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>

Other Baselines

• VanillaKD

Citation

@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}
}