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Gabriele Sarti

gsarti

https://gsarti.com

AI & ML interests

Interpretability for generative language models

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upvoted an article about 1 hour ago

The Transformers Library: standardizing model definitions

reacted to prithivMLmods's post with 👍 about 1 hour ago

OpenAI, Google, Hugging Face, and Anthropic have released guides and courses on building agents, prompting techniques, scaling AI use cases, and more. Below are 10+ minimalistic guides and courses that may help you in your progress. 📖 ⤷ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion ⤷ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents ⤷ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf ⤷ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering ⤷ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders ⤷ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide ⤷ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview ⤷ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf ⤷ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf ⤷ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf by HF🤗 : ⤷ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction ⤷ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents ⤷ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction ⤷ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn

upvoted an article about 1 hour ago

*Context Is Gold to Find the Gold Passage*: Evaluating and Training Contextual Document Embeddings

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@victor unprompted feature request: I'd love to have a toggle for a HF collection to control whether new items are added to the top or to the bottom. At the moment everything gets added at the bottom, but it would be great to have newer elements on top to make fresh content easily accessible without having to scroll all the way!

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🔍 Today's (self-serving) pick in Interpretability & Analysis of LMs:

A Primer on the Inner Workings of Transformer-based Language Models
by @javifer @gsarti @arianna-bis and M. R. Costa-jussà
( @mt-upc , @GroNLP , @facebook )

This primer can serve as a comprehensive introduction to recent advances in interpretability for Transformer-based LMs for a technical audience, employing a unified notation to introduce network modules and present state-of-the-art interpretability methods.

Interpretability methods are presented with detailed formulations and categorized as either localizing the inputs or model components responsible for a particular prediction or decoding information stored in learned representations. Then, various insights on the role of specific model components are summarized alongside recent work using model internals to direct editing and mitigate hallucinations.

Finally, the paper provides a detailed picture of the open-source interpretability tools landscape, supporting the need for open-access models to advance interpretability research.

📄 Paper: A Primer on the Inner Workings of Transformer-based Language Models (2405.00208)

🔍 All daily picks: https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-ofc-lms-65ae3339949c5675d25de2f9

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