Large Language Models Bootcamp - Day 4 Highlights

𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐋𝐋𝐌 𝐌𝐚𝐬𝐭𝐞𝐫𝐲: 𝐃𝐚𝐲 𝟒 𝐚𝐭 𝐭𝐡𝐞 𝐁𝐨𝐨𝐭𝐜𝐚𝐦𝐩! 🚀 Our participants delved deep into the world of LLMs, mastering both theory and hands-on exercises! Here’s a detailed breakdown of what they learned throughout the day: 𝟏. 𝐋𝐋𝐌𝐎𝐩𝐬: 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 with Bernease Herman 🌐 o 𝐒𝐞𝐭𝐭𝐢𝐧𝐠 𝐔𝐩 𝐓𝐡𝐫𝐞𝐬𝐡𝐨𝐥𝐝𝐬: Participants learned how to establish thresholds for various activities such as detecting malicious prompts and preventing data breaches. o 𝐌𝐨𝐝𝐞𝐥 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠: Techniques and limitations of fine-tuning LLMs were explored. o 𝐌𝐨𝐝𝐞𝐥 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐒𝐞𝐫𝐯𝐢𝐧𝐠: Best practices for deploying and serving LLMs in production environments were covered. o 𝐆𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬: Rules for prompt and response governance to ensure safe and appropriate interactions were set. o 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: LLM performance was tested with known prompts to identify and mitigate issues. o 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲: The internal state of LLMs was monitored using telemetry data to ensure consistent and predictable behavior. o 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐄𝐱𝐞𝐫𝐜𝐢𝐬𝐞: Applying techniques to real-world scenarios for building observability-first LLM applications. 𝟐. 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐋𝐋𝐌𝐬 with Raja Iqbal 🔧 o 𝐑𝐚𝐭𝐢𝐨𝐧𝐚𝐥𝐞 𝐟𝐨𝐫 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠: Raja explained the critical rationale behind fine-tuning LLMs, distinguishing it from transfer learning and highlighting its importance for specific applications. o 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬: Various techniques were covered, including repurposing, supervised vs. unsupervised fine-tuning, RLHF (Reinforcement Learning with Human Feedback), and PEFT (Parameter-Efficient Fine-Tuning). o 𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐋𝐚𝐛𝐬: Participants engaged in practical labs where they fine-tuned the Llama 2-7B model on GPU Pods provided by RunPod, reinforcing their theoretical understanding through hands-o