Where is a Demo for the ChatLaw2-MoE model described in the Readme?

[MartialTerran]

Hi. This is a great project! Where is the public demo or weights and MoE_model.py for the Mixture of Experts ChatLaw2-MoE model? Since the MOE_model has moderate size(? parameters), can you publish a pure-python MOE_model.py that does not depend upon "from transformers import"? Providing a pure standalone MOE_model.py (independent of the bulky transformers.py) would facilitate local installation/operation with the user's own RAG dataset (of statutes and caselaw) including edge-device operation.

Demonstrated: “Role of LLMs in the Future of Legal Practice”

“LLMs’ transformative potential in the legal field is evident from their impressive performance in legal exams. GPT-4 scored in the 90th percentile on the Uniform Bar Examination [61], and ChatGPT autonomously passed four law school final exams at a top law school [383]. These achievements showcase the significant impact of AI language models on legal practice. … LLMs [augmented by document Retrieval] can serve as a valuable tool for initial research, explanations, and improving efficiency in legal practice. ”
LLM_Survey_2015_onwards_arxiv.pdf available at:
https://www.techrxiv.org/doi/full/10.36227/techrxiv.23589741.v6

Can you specify confirm that only about 28B (4x7B) parameters were in the MoE ChatLaw model that exceeded performance of ChatGPT-4 on the "Unified Qualification Exam" Bar Exam? Did that MOE model have both English and Chinese language-tokens and comprehension/pretraining, or only English corresponding to the text of the Unified Qualification Exam" Bar Exam?
ChatLaw, an open-source MoE LLM, boasts even higher performance on BAR Exams than ChatGPT-4: “Our MoE model outperforms GPT-4 in the Lawbench and Unified Qualification Exam for Legal Professionals by 7.73% in accuracy” [using a model having only _?__B parameters “Based on the InternLM architecture with a 4x7B Mixture of Experts (MoE) design”] https://github.com/PKU-YuanGroup/ChatLaw/blob/main/README.md
https://arxiv.org/abs/2306.16092 https://huggingface.co/papers/2306.16092