| I work in the team at Meta building Llama models. We are looking to better understand how we can improve Llama models. We want to understand your challenges to build open source AI tools that help you the most. Your input is informing our roadmap and how we support the community to help accelerate adoption. Your feedback will be invaluable in shaping our product roadmap and potentially guiding the content of these community support initiatives, including possibly exploring hosting workshops focused on common Llama use cases and challenges. Here's what we'd love to understand: If you're using any models: 1. Most important considerations for LLM selection? 2. What models are you using in production? If not Llama, why not? If you're using Llama: 3. Use case description for Llama? 4. Llama Performance: Strengths? Weaknesses? Improvement areas? 5. Feature requests? 6. Deployment challenges with Llama? 7. AI stack: RAG, fine-tuning, agentic flows? 8. Data types you're working with? 9. Model fine-tuning challenges? 10. Gaps in developer tools? |