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Rajdeep Borgohain

rbgo
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AI & ML interests

Solving language barriers.

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Reacted to m-ric's post with 👍 3 days ago
🔍 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
updated a dataset 12 days ago
rbgo/llm-inference-benchmark
liked a Space 26 days ago
lj1995/GPT-SoVITS-v2
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