Finally, the video shows prompting the LLM before and after fine-tuning.After fine-tuning, the model is able to generate the reasoning tokens in French before generating the final response in English.Check this 👇 https://t.co/v5eluK4xLK ...

AI Engineering Landscape - The AI engineering community is broad, technical, and growing, with the "AI Engineer" title expected to gain more ground [5] - Many seasoned software developers are AI newcomers, with nearly half of those with 10+ years of experience having worked with AI for three years or less [7] LLM Usage and Customization - Over half of respondents are using LLMs for both internal and external use cases, with OpenAI models dominating external, customer-facing applications [8] - LLM users are leveraging them across multiple use cases, with 94% using them for at least two and 82% for at least three [9] - Retrieval-Augmented Generation (RAG) is the most popular customization method, with 70% of respondents using it [10] - Parameter-efficient fine-tuning methods like LoRA/Q-LoRA are strongly preferred, mentioned by 40% of fine-tuners [12] Model and Prompt Management - Over 50% of respondents are updating their models at least monthly, with 17% doing so weekly [14] - 70% of respondents are updating prompts at least monthly, and 10% are doing so daily [14] - A significant 31% of respondents lack any system for managing their prompts [15] Multimodal AI and Agents - Image, video, and audio usage lag text usage significantly, indicating a "multimodal production gap" [16][17] - Audio has the highest intent to adopt among those not currently using it, with 37% planning to eventually adopt audio [18] - While 80% of respondents say LLMs are working well, less than 20% say the same about agents [20] Monitoring and Evaluation - Most respondents use multiple methods to monitor their AI systems, with 60% using standard observability and over 50% relying on offline evaluation [22] - Human review remains the most popular method for evaluating model and system accuracy and quality [23] - 65% of respondents are using a dedicated vector database [24] Industry Outlook - The mean guess for the percentage of the US Gen Z population that will have AI girlfriends/boyfriends is 26% [27] - Evaluation is the number one most painful thing about AI engineering today [28]

[Music] Thanks Alex for the introduction. That was a very impressive LLM generated summary of me. Uh I've never heard it before but uh nice.Um so um today I'm going to talk to you about something that has been keeping me up at night. Uh probably some of you too. So how to build enterprise aware agents.How to bring the brilliance of AI into the messy complex realities of uh how your business operated. So let's jump straight to the hottest question of the month for AI builders. Uh should I build workflows or ...

Those were the 4 stages of training an LLM from scratch.- Start with a randomly initialized model.- Pre-train it on large-scale corpora.- Use instruction fine-tuning to make it follow commands.- Use preference & reasoning fine-tuning to sharpen responses.Check this 👇 https://t.co/y273TGiFGM ...

RT Avi Chawla (@_avichawla)4 machine learning training paradigms, explained visually:- Transfer Learning- Fine-tuning- Multi-task Learning- Federated Learning https://t.co/q6wnDTQtIn ...

4 machine learning training paradigms, explained visually:- Transfer Learning- Fine-tuning- Multi-task Learning- Federated Learning https://t.co/q6wnDTQtIn ...

What goes into fine-tuning models like DeepSeek-R1?- Dataset preparation- Defining LoRA config- Defining a Trainer- Fine-tuning the LLM- Exporting to OllamaThe thread below covers this: https://t.co/78A3YXiWSyAvi Chawla (@_avichawla):Let's fine-tune DeepSeek-R1 (distilled Llama) 100% locally: ...

Full workshop covering all forms of fine-tuning and prompt engineering, like SFT, DPO, RFT, prompt engineering / optimization, and agent scaffolding. About Ilan Bigio Ilan Bigio is a founding member of OpenAI’s Developer Experience team where he explores model capabilities, builds demos and developer tools, and shares his learnings through talks and docs. His work includes creating the AI phone ordering demo showcased at DevDay 2024, leading technical development for Swarm, the precursor to the Agents SDK, ...