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Recent GitHub Issues Activity

_{Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.}

Quantify commits

Quantified Commit Activity Over 30 Days

Developer	Avatar	Branches	PRs	Commits	Files	Changes
Zhi Zheng		1	0/0/0	8	59	5863
LDLINGLINGLING		1	2/1/0	5	2	660
ywfang		1	0/0/0	1	2	8
n1majne3		1	1/1/0	2	1	6

_{PRs: created by that dev and opened/merged/closed-unmerged during the period}

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Recent Activity Analysis

Recent GitHub issue activity in the OpenBMB/MiniCPM repository indicates a focus on technical support, bug fixes, and feature requests. There is a notable emphasis on troubleshooting issues related to model deployment, compatibility with different platforms, and fine-tuning processes. Several users have reported difficulties with model conversion, inference errors, and deployment on edge devices.

Several issues highlight anomalies such as missing files during model conversion (#238), incorrect method calls leading to errors (#237), and ImportError due to version mismatches (#236). These issues suggest potential gaps in documentation or compatibility that may need addressing. Additionally, there are recurring themes of users seeking guidance on fine-tuning models (#229) and deploying them on various platforms, including Android and Docker.

Issue Details

1. #238: Created 0 days ago - A user reported an error during fine-tuning using llamafactory due to a missing file (data/dataset_info.json). This issue is critical as it blocks the fine-tuning process.

2. #237: Created 1 day ago - A bug was reported regarding an incorrect method call in dataset.py, where a list object is incorrectly assumed to have a shape attribute. This issue is labeled as a bug and requires code correction.

3. #236: Created 1 day ago - An ImportError was encountered due to a version mismatch in the vllm package. The user seeks clarification on the required version for compatibility.

4. #234: Created 2 days ago - A general question about the evaluation framework used for MiniCPM3-4B, indicating interest in performance benchmarking.

5. #232: Created 2 days ago - A bug report about installation errors under WSL when installing vllm, highlighting potential platform-specific issues.

6. #231: Created 2 days ago - A feature request for a Docker image for OpenBMB/llama.cpp, suggesting user interest in containerized deployments.

7. #230: Created 2 days ago - A bad case report about deployment errors with the reranker model, indicating potential integration issues with external services.

These issues reflect ongoing efforts to enhance model usability across different environments and improve documentation to address common user challenges.

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Overview

The dataset provides information on pull requests (PRs) for the OpenBMB/MiniCPM repository, which focuses on developing efficient language models for edge devices. The data includes one open PR and 33 closed PRs, detailing various updates and enhancements to the MiniCPM project.

Summary of Pull Requests

1. #233: Open - Adapted finetune code for MiniCPM3.0, tested with MiniCPM2.0 and lora training without issues.
2. #217: Closed - Enhanced compatibility of quantization code from MiniCPM2.0 to MiniCPM3.0.
3. #209: Closed - Minor README.md fix related to llama.cpp.
4. #183: Closed - Added tutorial links to README files.
5. #180: Closed - Added xtuner community link to README files.
6. #177: Closed - Added LLaMA-Factory navigation to README files.
7. #176: Closed - Introduced qlora training method and updated documentation.
8. #172: Closed - Added langchain demo for multi-file RAG on low-memory GPUs.
9. #170: Closed - Updated README-en with quick navigation and additional content.
10. #169: Closed - Added bnb quantization and quick navigation.
11. #166: Closed - Introduced powerinfer deployment example for MiniCPM-S-1B model.
12. #162: Closed - Fixed two bugs in mlx module related to user tokens and function naming conflicts.
13. #161: Closed - Added LLaMA-Factory example for fine-tuning MiniCPM.
14. #157: Closed - Added autoawq support for MiniCPM with quantification datasets.
15. #156: Closed - Adjusted user tokens for different models to avoid issues during training.
16. #145: Closed - Added ollama support for MiniCPM-1B, including usage instructions in README.
17. #134: Closed (Not merged) - Modified finetune.py to fix token error in model embedding.
18. #126: Closed - Adapted code for ollama compatibility with MiniCPM-2B-DPO model.
19. #122: Closed - Added OpenAI API support, tested regular conversation features.
20. #111: Closed - Supported MiniCPMV model in hf_demo with visual results.
21. #110: Closed - Created a small demo for mlx inference on macOS.
22. #106: Closed - Added fine-tuning model settings for bf16 and fp16 precision (#92).
23. #79: Closed - Supported fastllm integration.
24. #53: Closed - Updated evaluation table for MiniCPM-V in documentation.
25. #52, #35, #28, #25, #21, #19, #16, #14, #2, and #1: Various closed PRs focused on documentation updates, feature additions, bug fixes, and infrastructure improvements.

Analysis of Pull Requests

The pull request activity in the OpenBMB/MiniCPM repository reflects a dynamic development environment focused on enhancing the capabilities of the MiniCPM models while ensuring compatibility across different versions and platforms. A significant theme is the continuous adaptation of existing functionalities to newer versions of the model, as seen in PRs like #217 and #233, which focus on adapting quantization and fine-tuning code for MiniCPM3.0.

Documentation updates are frequent, indicating an emphasis on maintaining comprehensive guides for users and developers alike (e.g., PRs #209, #183). This aligns with the project's goal of fostering community engagement by providing clear instructions and resources.

Several PRs introduce new features or improve existing ones, such as the addition of qlora training (#176) and langchain demos (#172), highlighting ongoing efforts to expand the model's applicability and ease of use in diverse scenarios.

Bug fixes are also prevalent, addressing issues that could hinder model performance or user experience (e.g., PR #162). The prompt resolution of these bugs suggests a responsive development team attentive to maintaining software quality.

Anomalies include PR #134, which was closed without merging due to unresolved issues with token handling in fine-tuning scripts, suggesting potential areas where further refinement is needed.

Overall, the repository's activity demonstrates a robust commitment to evolving the MiniCPM models through iterative improvements, community-driven feedback, and strategic feature enhancements aimed at optimizing performance on edge devices while maintaining competitive capabilities against larger language models.

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Development Team and Recent Activity

Team Members and Activities

LDLINGLINGLING

• Commits: 5 commits with 660 changes across 2 files.
• Recent Work: Focused on adapting quantization code from MiniCPM2.0 to MiniCPM3.0, fixing bugs in the awq_data, and updating the README.
• Collaboration: Merged pull requests from other contributors like n1majne3.
• In Progress: No specific ongoing work mentioned.

n1majne3

• Commits: 2 commits with 6 changes in the README file.
• Recent Work: Minor updates to the README.md file.
• Collaboration: Submitted a patch that was merged by LDLINGLINGLING.

zh-zheng

• Commits: 8 commits with 5863 changes across 59 files.
• Recent Work: Updated README files, refactored demo folders, and made significant additions to documentation and assets.
• Collaboration: Worked independently on documentation and demo updates.

SUDA-HLT-ywfang

• Commits: 1 commit with 8 changes across 2 files.
• Recent Work: Updated bfcl/livecodebench version in README files.
• Collaboration: No direct collaboration noted in recent commits.

Patterns, Themes, and Conclusions

1. Active Development on Quantization: There is a clear focus on adapting and enhancing quantization code for MiniCPM3.0, indicating ongoing efforts to optimize model performance for edge devices.

2. Documentation and Demo Updates: Significant activity around updating documentation and demos suggests an emphasis on improving user experience and accessibility of the project.

3. Collaborative Efforts: While there are instances of collaboration, such as LDLINGLINGLING merging contributions from others, much of the work appears to be conducted independently by team members.

4. Bug Fixes and Maintenance: Regular bug fixes and updates to existing features reflect a commitment to maintaining the stability and reliability of the project.

Overall, the recent activities indicate a balanced focus on both feature development (particularly around quantization) and improving project documentation and usability.