Open XinrunDu opened 2 months ago
Dear M-A-P Team,
Thank you for reading our technical report and for your valuable feedback. We appreciate your kind words and understand that our report may not have been as formal as it could have been. We apologize for any confusion this may have caused.
We greatly appreciate your suggestion to compare our model with CT-LLM and MAP-Neo. Your input is invaluable, and we will certainly take it into account as we move forward.
Once again, thank you for your interest in our work. We eagerly anticipate future opportunities to collaborate and exchange ideas.
Best regards,
Index Team
Dear M-A-P Team,
Thank you for reading our technical report and for your valuable feedback. We appreciate your kind words and understand that our report may not have been as formal as it could have been. We apologize for any confusion this may have caused.
We greatly appreciate your suggestion to compare our model with CT-LLM and MAP-Neo. Your input is invaluable, and we will certainly take it into account as we move forward.
Once again, thank you for your interest in our work. We eagerly anticipate future opportunities to collaborate and exchange ideas.
Best regards,
Index Team
Dear Index Team,
Thank you for your prompt and considerate response. We are pleased to hear that you found our suggestions helpful. We look forward to any future collaborations and the opportunity to exchange ideas with your team.
Best regards,
M-A-P Team
Dear Index Team,
I hope this message finds you well. Your contributions are highly valuable to the community, and I appreciate the effort you have put into this project.
I was wondering if you have considered comparing your model to other similar works such as CT-LLM, and MAP-Neo (which has also released a 2B size model). These models, like yours, contribute precious training resources to the open-source bilingual language model community. A comparison could provide interesting insights and further highlight the strengths of your model.
Thank you once again for your outstanding contributions. I look forward to seeing your future advancements in this area.
CT-LLM: Chinese Tiny LLM (chinese-tiny-llm.github.io) MAP-Neo: MAP-Neo (map-neo.github.io)
Best regards,
M-A-P Team