EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 16·5 min read
Big Pickle on SWE Atlas – Codebase QnA
TL;DR
A free stealth model on OpenCode Zen outperformed all entries on the SWE Atlas Codebase QnA benchmark, except for two Claude models. This achievement highlights significant advancements in codebase question-and-answer capabilities.
✦ Why It Matters
Consider integrating the new model into your codebase QnA systems for improved performance.
Key Takeaways
- Evaluate Big Pickle for coding tasks in TypeScript and Python.
- Be aware of the single-trial evaluation limitations.
- Monitor updates on Big Pickle's model identity and performance.
- Utilize the provided configurations for reproducibility in testing.
Full Summary
Big Pickle is a free AI model tested on Scale AI's SWE Atlas Codebase QnA benchmark, which assesses the model's ability to resolve coding tasks. In a recent evaluation, it achieved a task resolve rate of 50.8%, successfully answering 63 out of 124 questions.
This performance places it third overall on the leaderboard, surpassing other models in the Mini-SWE-Agent category and only trailing behind two Claude models. The evaluation utilized the mini-swe-agent scaffold and followed strict protocols, including a single trial per task and specific resource constraints.
Notably, Big Pickle excelled in TypeScript and Python tasks, with resolve rates of 58.1% and 55.2%, respectively. However, the results should be interpreted cautiously due to the single-trial nature of the evaluation and the model's unconfirmed identity.