LLM Colosseum, a benchmarking tool for evaluating large language models through Street Fighter III competitions, has seen minimal recent development activity, with significant user issues remaining unresolved.
The project currently has 15 open issues, primarily concerning model performance and configuration difficulties. Users frequently report challenges with local model setups and environment configurations, suggesting a need for improved documentation or support. Notable issues include #46, where characters fail to approach each other, and #47, which questions the ELO ranking system. These issues indicate potential bugs and ongoing discussions about performance metrics.
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 0 | 0 | 0 | 0 | 0 |
| 30 Days | 0 | 0 | 0 | 0 | 0 |
| 90 Days | 4 | 3 | 16 | 4 | 1 |
| All Time | 29 | 14 | - | - | - |
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.
The OpenGenerativeAI/llm-colosseum repository has seen consistent activity, with 15 open issues currently reported. Notably, many issues revolve around model performance, configuration errors, and user queries about integrating new models. A recurring theme is the struggle with local model setups and the intricacies of the environment configuration, indicating a need for clearer documentation or support.
Several issues highlight critical gaps in user experience, such as difficulties in running local models and obtaining correct ROM files for gameplay. The presence of unresolved technical queries suggests that while the community is engaged, there may be a bottleneck in addressing these concerns effectively.
Issue #47: ELO ranking score?
Issue #46: [question] Two characters cannot approach each other after they switch positions.
Issue #43: Add Google Gemini model
Issue #42: How to use Google gemini model
Issue #41: Hello, brother. How to modify the program so that AI can play computer-controlled characters?
Issue #40: Is there a way to set it to do best 3 of 5?
Issue #39: Yi 6b, no action
Issue #38: how to set show_final=true???
Issue #37: suggestion: add blood in log
Issue #35: Report Different models fight on the street
The issues predominantly reflect a mix of technical inquiries and feature requests, with users actively seeking assistance on configuration and model integration. This indicates a vibrant but potentially overwhelmed community where users are eager to engage but face hurdles in implementation.
The recurring nature of setup-related issues suggests that improving documentation or providing more robust support could alleviate many of these concerns and enhance overall user satisfaction.
This analysis highlights key areas for improvement within the project, focusing on technical support and user engagement strategies to foster a more collaborative development environment.
The analysis of the pull requests (PRs) for the LLM Colosseum project reveals a robust and active development process. The project has seen a total of 38 closed PRs, indicating a healthy level of maintenance and feature enhancement. The PRs cover a range of updates from adding new features and fixing bugs to simplifying code and updating documentation.
Summary
Quantified Reports
Detailed Reportsllm.py and requirements.txt. Merged 34 days ago.requirements.txt and made minor adjustments in other files. Merged 34 days ago.Street Fighter III. Merged 34 days ago.ollama.py to local.py as part of issue resolution. Merged 44 days ago.Dockerfile and docker-compose.yml, enhancing deployment options. Merged 44 days ago.dashboard.ipynb. Merged 51 days ago.The PRs reflect a strong focus on expanding the functionality and improving the usability of the LLM Colosseum project. Notable trends include:
Feature Expansion: Several PRs (#66 for Cerebras API and #59 for Amazon Bedrock) indicate ongoing efforts to integrate more LLMs into the benchmarking framework. This is crucial for maintaining the project's relevance as new models emerge.
Infrastructure Improvements: The introduction of Docker support (#64) suggests an emphasis on making the setup process easier and more consistent across different environments. This is particularly important for community engagement and contribution.
Code Maintenance and Simplification: PRs like #67 (simplifying requirements) and PR #68 (adding back mistral support) show a commitment to keeping the codebase clean and manageable. This is essential for long-term sustainability.
Bug Fixes and Compatibility Updates: Regular updates to address bugs (#61 fixing Pydantic issues) demonstrate proactive maintenance efforts to ensure compatibility with dependencies.
Community Contributions: The presence of contributions from various developers (e.g., João Galego in PR #59) highlights an active community around the project. However, some draft PRs (#48 & PR #45) remain unmerged for an extended period, which could indicate either pending decisions or potential issues that need resolution.
Documentation and Minor Fixes: Several PRs focus on documentation updates (#58) or minor fixes (#55), which are important for maintaining clarity and accuracy in project documentation.
In conclusion, the LLM Colosseum project exhibits a healthy development lifecycle with a clear focus on feature expansion, infrastructure improvement, code maintenance, and community engagement. The active management of pull requests suggests a well-organized approach to software development that prioritizes both functionality and usability.
Nicolas Oulianov (oulianov)
Vithu Thangarasa (vithursant)
Stan Girard (StanGirard)
Nick Schuetz (nickschuetz)
João Galego (JGalego)
Pierre-Louis Biojout (Pierre-LouisBJT)
PL Venard (Platinn)
Sam Pink (SamPink)
Ikko Eltociear Ashimine (eltociear)
Zedmat (harshkasat)