Hugging Face's logo
Asankhaya Sharma's picture
68 107 223

Asankhaya Sharma

codelion
jasgigli's profile picture anderson9876's profile picture hitchhiker3010's profile picture
·
http://asankhaya.github.io/

AI & ML interests

Creator of OptiLLM, OpenEvolve, Adaptive Classifier, and PTS. Pioneering a new category in AI infrastructure: inference-time compute for LLMs.

Recent Activity

reacted to their post with ➕ 3 days ago
🧬 We just published our comprehensive analysis of OpenEvolve - an open-source evolutionary coding agent that automatically optimizes algorithms using LLMs! Our key findings from 29 experiments across 10 models: - Gemini Flash 2.5 achieved 2.04x speedup across 30 benchmark tasks - Open models like Gemma 3 27B (1.63x) and Qwen3-Coder 480B (1.41x) rivaled proprietary models - The system discovered entirely new algorithms - not just code optimizations! - One task evolved from DFS to BFS to Union-Find approaches - Specialized coding models outperformed much larger general models 200 iterations beat 100 iterations by 24% - Ensembles surprisingly failed due to conflicting optimization strategies Most fascinating: watching models evolve code step-by-step, like transforming matrix operations from basic eigendecomposition to vectorized one-liners with 32x speedup. Our systematic experimental approach reveals that open-source evolutionary coding is becoming seriously competitive with proprietary solutions. We tested everything from temperature settings to evolution strategies to find optimal configurations. This research shows automated code optimization is ready for real-world applications. The methodology we developed can guide anyone building evolutionary coding systems. Full paper with code examples, detailed methodology, and all experimental results: https://huggingface.co/blog/driaforall/towards-open-evolutionary-agents What optimization challenges could benefit from evolutionary approaches in your work?
reacted to their post with 🤗 3 days ago
🧬 We just published our comprehensive analysis of OpenEvolve - an open-source evolutionary coding agent that automatically optimizes algorithms using LLMs! Our key findings from 29 experiments across 10 models: - Gemini Flash 2.5 achieved 2.04x speedup across 30 benchmark tasks - Open models like Gemma 3 27B (1.63x) and Qwen3-Coder 480B (1.41x) rivaled proprietary models - The system discovered entirely new algorithms - not just code optimizations! - One task evolved from DFS to BFS to Union-Find approaches - Specialized coding models outperformed much larger general models 200 iterations beat 100 iterations by 24% - Ensembles surprisingly failed due to conflicting optimization strategies Most fascinating: watching models evolve code step-by-step, like transforming matrix operations from basic eigendecomposition to vectorized one-liners with 32x speedup. Our systematic experimental approach reveals that open-source evolutionary coding is becoming seriously competitive with proprietary solutions. We tested everything from temperature settings to evolution strategies to find optimal configurations. This research shows automated code optimization is ready for real-world applications. The methodology we developed can guide anyone building evolutionary coding systems. Full paper with code examples, detailed methodology, and all experimental results: https://huggingface.co/blog/driaforall/towards-open-evolutionary-agents What optimization challenges could benefit from evolutionary approaches in your work?
reacted to their post with 🚀 3 days ago
🧬 We just published our comprehensive analysis of OpenEvolve - an open-source evolutionary coding agent that automatically optimizes algorithms using LLMs! Our key findings from 29 experiments across 10 models: - Gemini Flash 2.5 achieved 2.04x speedup across 30 benchmark tasks - Open models like Gemma 3 27B (1.63x) and Qwen3-Coder 480B (1.41x) rivaled proprietary models - The system discovered entirely new algorithms - not just code optimizations! - One task evolved from DFS to BFS to Union-Find approaches - Specialized coding models outperformed much larger general models 200 iterations beat 100 iterations by 24% - Ensembles surprisingly failed due to conflicting optimization strategies Most fascinating: watching models evolve code step-by-step, like transforming matrix operations from basic eigendecomposition to vectorized one-liners with 32x speedup. Our systematic experimental approach reveals that open-source evolutionary coding is becoming seriously competitive with proprietary solutions. We tested everything from temperature settings to evolution strategies to find optimal configurations. This research shows automated code optimization is ready for real-world applications. The methodology we developed can guide anyone building evolutionary coding systems. Full paper with code examples, detailed methodology, and all experimental results: https://huggingface.co/blog/driaforall/towards-open-evolutionary-agents What optimization challenges could benefit from evolutionary approaches in your work?
View all activity

Organizations

meraGPT's profile picture Lambda Security's profile picture National University of Singapore's profile picture Patched's profile picture ZeroGPU Explorers's profile picture MLX Community's profile picture Social Post Explorers's profile picture Hugging Face Discord Community's profile picture Dria's profile picture Adaptive Classifier's profile picture Reasoning datasets competition 's profile picture Cerebras Hugging Face Hackathon's profile picture Agents-MCP-Hackathon's profile picture