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Aymeric Roucher

m-ric

http://aymeric-roucher.github.io

AI & ML interests

MLE at Hugging Face 🤗 LLMs, Agents, RAG, Multimodal.

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274

🔍 Meta teams use a fine-tuned Llama model to fix production issues in seconds

One of Meta's engineering teams shared how they use a fine-tuned small Llama (Llama-2-7B, so not even a very recent model) to identify the root cause of production issues with 42% accuracy.

🤔 42%, is that not too low?
➡️ Usually, whenever there's an issue in production, engineers dive into recent code changes to find the offending commit. At Meta's scale (thousands of daily changes), this is like finding a needle in a haystack.
💡 So when the LLM-based suggestion is right, it cuts incident resolution time from hours to seconds!

How did they do it?

🔄 Two-step approach:
‣ Heuristics (code ownership, directory structure, runtime graphs) reduce thousands of potential changes to a manageable set
‣ Fine-tuned Llama 2 7B ranks the most likely culprits

🎓 Training pipeline:
‣ Continued pre-training on Meta's internal docs and wikis
‣ Supervised fine-tuning on past incident investigations
‣ Training data mimicked real-world constraints (2-20 potential changes per incident)

🔮 Now future developments await:
‣ Language models could handle more of the incident response workflow (runbooks, mitigation, post-mortems)
‣ Improvements in model reasoning should boost accuracy further

Read it in full 👉 https://www.tryparity.com/blog/how-meta-uses-llms-to-improve-incident-response

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1184

Great feature alert: 𝗬𝗼𝘂 𝗰𝗮𝗻 𝗻𝗼𝘄 𝘂𝘀𝗲 𝗮𝗻𝘆 𝗦𝗽𝗮𝗰𝗲 𝗮𝘀 𝗮 𝘁𝗼𝗼𝗹 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀.𝗮𝗴𝗲𝗻𝘁! 🛠️🔥🔥

This lets you take the coolest spaces, like FLUX.1-dev, and use them in agentic workflows with a few lines of code! 🧑‍💻

On the video below, I set up my fake vacation pictures where I'm awesome at surfing (I'm really not) 🏄

Head to the doc to learn this magic 👉 https://huggingface.co/docs/transformers/main/en/agents_advanced#import-a-space-as-a-tool-

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