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Mert Erbak PRO

merterbak

AI & ML interests

NLP and Image Processing

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updated a model about 2 hours ago

merterbak/Mistral-Small-3.1-24B-Instruct-2503-GGUF

reacted to their post with 🔥 about 12 hours ago

FlowReasoner is a new system that builds a custom set of small AI agents for every user question. Unlike search based methods it uses reasoning driven optimization with external execution feedback. ✅ First, it distills reasoning data using DeepSeek R1-671B to build multi agent systems. 🤖 ✅ Then, reasoning data used for DeepSeek-R1-Distill-Qwen-7B via supervised fine tuning for basic reasoning skills. 💡 ✅ Finally, RL with GRPO (optimizes by comparing response groups from queries/tasks) to improve reasoning. https://huggingface.co/papers/2504.15257 Code: https://github.com/sail-sg/flowreasoner

reacted to their post with ❤️ about 12 hours ago

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3077

FlowReasoner is a new system that builds a custom set of small AI agents for every user question. Unlike search based methods it uses reasoning driven optimization with external execution feedback.

✅ First, it distills reasoning data using DeepSeek R1-671B to build multi agent systems. 🤖
✅ Then, reasoning data used for DeepSeek-R1-Distill-Qwen-7B via supervised fine tuning for basic reasoning skills. 💡
✅ Finally, RL with GRPO (optimizes by comparing response groups from queries/tasks) to improve reasoning.

FlowReasoner: Reinforcing Query-Level Meta-Agents (2504.15257)
Code: https://github.com/sail-sg/flowreasoner

Post

2048

Here’s a cool paper I found: “Massive Image Embedding Benchmark (MIEB).” It is a new tool to test how good image embedding models are. It has 130 different tasks grouped into 8 categories, like image search, classification, clustering similar images, answering questions based on images, and understanding documents. It even covers 38 different languages.

The authors tested 50 models and found that no single model was best at everything. Some models were great at recognizing text inside images but struggled to handle complicated tasks like matching images and text that appear together.

Paper: https://arxiv.org/pdf/2504.10471v1
Code: https://github.com/embeddings-benchmark/mteb

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