Downloads · 30 days
75
25% of all-time downloads
Intel/LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound
LongCat-Flash-Lite-Sparse-MXFP4-CT-AutoRound is a text generation model from Intel. Use it when you need the model to write or continue text. It is set up for LongCat-Flash-Lite-Sparse. The card lists the license as mit.
This model is an mxp4 model of LongCat-Flash-Lite-Sparse generated by intel/auto-round with RTN mode. Please follow the license of the original model.
Downloads · 30 days
75
25% of all-time downloads
All-time downloads
298
Public
Parameters
69.1B
88.6 GB on disk
Likes
1
Public
Click a slice to open those files.
.safetensors88.6 GB · 100%
How the weights are stored.
BF1635.3B · 51%
From the Hugging Face model README
This model is an mxp4 model of LongCat-Flash-Lite-Sparse generated by intel/auto-round with RTN mode. Please follow the license of the original model.
auto-round \
meituan-longcat/LongCat-Flash-Lite-Sparse \
--model_free \
--scheme MXFP8 \
--ignore_layers "lm_head,embed,self_attn,mlps,router,mtp" \
--layer_config '{mlp.experts:{bits:4,data_type:mx_fp}}' \
--format llm_compressor \
--output_dir ./LongCat-Flash-Lite-Sparse-MXFP4
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of the model, developers should perform safety testing.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Here are a couple of useful links to learn more about Intel's AI software:
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }