One more cookbook:
Agent for self-correcting Text-to-SQL What if the query generated by your Text-to-SQL pipeline is correct SQL but returns wrong results? We need to add a critique step That's very simple with an agent!
Check out the notebook!
@aymericroucher
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AI Agent for Self-Correcting Text-to-SQL Queries
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Agentic RAG with Transformers Agents Improves Retrieval Performance
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New cookbook! I show to to make agentic RAG using Transformers Agents. Compared to vanilla RAG, agentic RAG can: Reformulate the query Critique the retrived content to re-retrieve if needed Score increase of 8.5%! (Llama-3-70B-judge)
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Free AI Super-Resolution Model with 600M Parameters Released
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I remember when the "super resolution" filter used by Jack Bauer in 24 seemed like sci-fi bullshit.
— m_ric (@AymericRoucher) 2 juillet 2024
But now you have free models, 600M parameters, that can do precisely that 🤯https://t.co/wMuNrshTM1 pic.twitter.com/29dJetIjV3I remember when the "super resolution" filter used by Jack Bauer in 24 seemed like sci-fi bullshit. But now you have free models, 600M parameters, that can do precisely that https://
huggingface.co/fal/AuraSR -
Effectiveness of Plain Prompting vs Fine-Tuned Models for AI Agents
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It's trendy to share models "fine-tuned for function calling", e.g. Command-R-Plus or Mixtral-8x22B. But you don't need this to make good agents Cf graph:
The count of incorrectly formatted actions is already close to 0 with plain prompting! (GPT-4o, GAIA validation run) -
Google Releases Gemma-2: Leading Open-Source LLM
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Google just released Gemma-2. The 27B version: directly becomes the best open-source LLM as per Chatbot Arena Punches wayyy above its weight: I plotted Arena ELO vs model size below, it's crazy
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Code Agent Built with Transformers Agents Tops GAIA Leaderboard
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With @sergeipetrov we built a Code agent with Transformers Agents to beat the GAIA leaderboard. It worked well! Our submission scores #2 overall on the test set and #1 on the validation set. On both sets we are #1 on the hardest Level 3 questions, reaching nearly 20%.
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Exploring Concept Emergence in Large Language Models
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LLMs are huge piles of neurons that somehow give useful outputs, but at which points do real concepts emerge from this mathematical mess? @Anthropic team did fascinating work on that: read my summary here
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Building a React Code Agent for Transformer Agents
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Last week I've given a talk about building a React Code Agent for Transformers Agents! ➤ 𝗥𝗲𝗔𝗰𝘁: the agent iterates on previous actions in a 𝗥𝗲flection-𝗔𝗰𝘁ion cycle ➤ 𝗖𝗼𝗱𝗲: the agent writes actions as Code snippets, Read the slides
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React LLM Agent Uses Memory to Solve Tasks
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How can an agentic workflow use an LLM to solve tasks?
— m_ric (@AymericRoucher) 31 mai 2024
➡️ I made my first ever 𝘮𝘢𝘯𝘪𝘮 video to show that:
Watch below how a React LLM Agent solves a simple task, by leveraging its memory to iterate on previous actions! 📺👇 pic.twitter.com/a3S348qa0gHow can an agentic workflow use an LLM to solve tasks? I made my first ever 𝘮𝘢𝘯𝘪𝘮 video to show that:
Watch below how a React LLM Agent solves a simple task, by leveraging its memory to iterate on previous actions! -
New ReactCodeAgents Available via Transformers Agents Package
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Kind reminder that you can just 'pip install transformers[agents]' to try our shiny new ReactCodeAgents!