@juan8sho: Invitación a transformarnos desde el caos y la sombra a través del sello de la tormenta. #energia #maya #tzolkin #transformation #despertarespiritual

Juan8Shó⚡
Juan8Shó⚡
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Friday 26 June 2026 21:16:50 GMT
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Building a Large Language Model from scratch is an incredible feat—but most projects fail long before they hit production. As shown in the brilliant breakdown by *The World of Data* in image.png, a successful LLM journey relies on **5 critical stages**, each carrying its own hidden project-killers. ### 📍 The 5 Stages of LLM Development:  1. **Data Collection & Preprocessing:** Extracting, filtering (toxic, NSFW, PII), and deduplicating. *Mistake to avoid:* **Dirty Training Data.** If you feed the model biased or low-quality data, you poison it before a single weight is updated.  2. **Pre-Training (The Foundation):** Training a Decoder-Only Transformer on next-token prediction so it learns language, facts, and reasoning. *Mistake to avoid:* **Ignoring Scaling Laws.** Choosing the wrong model size for your token budget wastes massive compute.  3. **Supervised Fine-Tuning (SFT):** Turning a raw predictor into a helpful assistant using instruction-prompt pairs. *Mistake to avoid:* **Using GPT-Generated Data blindly.** Your model will simply inherit the parent model’s bias, mistakes, and failure modes.  4. **Alignment (RLHF / Constitutional AI):** Optimizing policies (via PPO or DPO) to ensure safer, helpful, and honest outputs. *Mistake to avoid:* **Skipping Alignment for Your Context.** Generic safety parameters won't hold up in complex agentic loops.  5. **Inference Optimization & Deployment:** Quantizing, compressing, and setting up APIs for monitoring and AI agents at scale. *Mistake to avoid:* **Optimizing for Benchmarks.** Great benchmark scores do not automatically equal great real-world behavior under production conditions. > 💡 **The Takeaway:** Building an LLM isn't just about throwing compute at a problem. It’s an intricate pipeline where data quality, alignment, and production-ready optimization matter just as much as the architecture itself. >  Which of these stages has your team found the most challenging to optimize? Let’s discuss in the comments! 👇 #GenerativeAI #LLM #MachineLearning #DataScience #AIEngineering
Building a Large Language Model from scratch is an incredible feat—but most projects fail long before they hit production. As shown in the brilliant breakdown by *The World of Data* in image.png, a successful LLM journey relies on **5 critical stages**, each carrying its own hidden project-killers. ### 📍 The 5 Stages of LLM Development: 1. **Data Collection & Preprocessing:** Extracting, filtering (toxic, NSFW, PII), and deduplicating. *Mistake to avoid:* **Dirty Training Data.** If you feed the model biased or low-quality data, you poison it before a single weight is updated. 2. **Pre-Training (The Foundation):** Training a Decoder-Only Transformer on next-token prediction so it learns language, facts, and reasoning. *Mistake to avoid:* **Ignoring Scaling Laws.** Choosing the wrong model size for your token budget wastes massive compute. 3. **Supervised Fine-Tuning (SFT):** Turning a raw predictor into a helpful assistant using instruction-prompt pairs. *Mistake to avoid:* **Using GPT-Generated Data blindly.** Your model will simply inherit the parent model’s bias, mistakes, and failure modes. 4. **Alignment (RLHF / Constitutional AI):** Optimizing policies (via PPO or DPO) to ensure safer, helpful, and honest outputs. *Mistake to avoid:* **Skipping Alignment for Your Context.** Generic safety parameters won't hold up in complex agentic loops. 5. **Inference Optimization & Deployment:** Quantizing, compressing, and setting up APIs for monitoring and AI agents at scale. *Mistake to avoid:* **Optimizing for Benchmarks.** Great benchmark scores do not automatically equal great real-world behavior under production conditions. > 💡 **The Takeaway:** Building an LLM isn't just about throwing compute at a problem. It’s an intricate pipeline where data quality, alignment, and production-ready optimization matter just as much as the architecture itself. > Which of these stages has your team found the most challenging to optimize? Let’s discuss in the comments! 👇 #GenerativeAI #LLM #MachineLearning #DataScience #AIEngineering

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