LLM Training for High-Quality Content Creation

Training a Large Language Model (LLM) involves more than just scaling up data and compute. It requires a disciplined approach across multiple layers of the ML lifecycle to ensure performance, efficiency, safety, and adaptability. This visual framework outlines eight critical pillars necessary for successful LLM training, each with a defined workflow to guide implementation: 𝟭. 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗖𝘂𝗿𝗮𝘁𝗶𝗼𝗻: Use diverse, clean, and domain-relevant datasets. Deduplicate, normalize, filter low-quality samples, and tokenize effectively before formatting for training. 𝟮. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Design efficient preprocessing pipelines—tokenization consistency, padding, caching, and batch streaming to GPU must be optimized for scale. 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝘀𝗶𝗴𝗻: Select architectures based on task requirements. Configure embeddings, attention heads, and regularization, and then conduct mock tests to validate the architectural choices. 𝟰. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 and 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Ensure convergence using techniques such as FP16 precision, gradient clipping, batch size tuning, and adaptive learning rate scheduling. Loss monitoring and checkpointing are crucial for long-running processes. 𝟱. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗠𝗲𝗺𝗼𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Leverage distributed training, efficient attention mechanisms, and pipeline parallelism. Profile usage, compress checkpoints, and enable auto-resume for robustness. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Regularly evaluate using defined metrics and baseline comparisons. Test with few-shot prompts, review model outputs, and track performance metrics to prevent drift and overfitting. 𝟳. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀: Mitigate model risks by applying adversarial testing, output filtering, decoding constraints, and incorporating user feedback. Audit results to ensure responsible outputs. 🔸 𝟴. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗗𝗼𝗺𝗮𝗶𝗻 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Adapt models for specific domains using techniques like LoRA/PEFT and controlled learning rates. Monitor overfitting, evaluate continuously, and deploy with confidence. These principles form a unified blueprint for building robust, efficient, and production-ready LLMs—whether training from scratch or adapting pre-trained models.

How are open LLMs trained and created in 2024? 🤔 01.AI just released their paper on how they created the YI, a family of LLMs and V-LLMs. The paper includes details on the data processing, training and multimodality part. Let's take a look👀 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲: 🌐 Start with web documents from Common Crawl and use the CCNet pipeline for language identification and perplexity scoring. ⚖️ Apply Heuristic rules to remove low-quality text based on URL, domain, word blocklists, garbled text, document length, special symbol ratio, short/consecutive/incomplete lines, and repeated words/n-grams/paragraphs. 📋 Use learned filters with perplexity scorer (KenLM), quality scorer (Wikipedia classifier), safety scorer, and document coherence scorer to identify and remove low-quality content. 🧩 Apply Clustering to group and analyze documents, removing low-quality ones. 📚 Run Deduplication with document-level MinHash deduplication and sub-document exact-match deduplication. 📙 Categorize web documents into specific themes using a topic model and down-sample less helpful content in the final pretraining dataset, e.g. advertisement 𝗠𝗼𝗱𝗲𝗹 𝗽𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴, 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗺𝗼𝗱𝗲𝗹 𝗺𝗲𝗿𝗴𝗶𝗻𝗴: 🧠 𝗣𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴: Started from Llama 2 architecture with Grouped-Query Attention (GQA), SwiGLU activation, and RoPE. Trained on the 3.1T pretraining corpora on 4k context with 65,000 vocablurary. Continued pertaining on 10B tokens with upsampled long sequences (5B) from books 👨🏻🎨 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴: Created a small (< 10K) dataset of multi-turn instruction-response dialog pairs, employing techniques like CoT, Evol-Instruct, and diversity-filtering. Supervised Fine-tuned the Base model using ChatML format for ~2 epochs. 🖼️ 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹𝗶𝘁𝘆: Applied a similar approach to Llava by adding a vision encoder (CLIP) to the chat model connected with an MLP. Trained the vision encoder and connector on 100M 224x224 image-text pairs. Then Scaled the resolution by continuing training on ~25M 448x448 pairs. Trained entire VLLM (Vision Encoder + LLM) on ~1M samples, achieving a MMMU score of 41.6 (GPT-4V 55.7) ⬆️ 𝗠𝗲𝗿𝗴𝗶𝗻𝗴: Yi-9B is upscale of Yi-6B by merging/duplicating the original 16 middle layers and continuing pretraining on 800B tokens (more code), leading to +5% on MMLU and +23% on HumanEval 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: 📉 Used rather a small (<10K) dataset, e.g. OpenHermes has 1M samples 📈 5B length-upsampled data is enough to extend the context 💻 6B fits on edge and 34B on 1x A100 📝 SFT responses are structured in an introduction-body-conclusion ❓ no information on data contamination as part of their data pipeline 🥊 34B YI can compete with Llama 2 70B ✨ Merging could be an effective way to adopt new domains/skills Arxiv: https://lnkd.in/eNiM5SGG Github: https://lnkd.in/esXbu-_g Model: https://lnkd.in/eFBGRdr2

Andrej Karpathy has released one of the most comprehensive guides on LLMs In just 3.5 hours, he dives deep into the architecture, training, and applications of LLMs. Here’s what makes this video a must-watch: 1. Evolution of Language Models Karpathy traces the journey from simple statistical methods to advanced neural networks like Transformers. He explains how these models are trained on vast datasets, enabling them to generate human-like text and perform tasks like translation and code generation. 2. Inner Workings Unveiled A significant part of the video breaks down complex concepts such as attention mechanisms, tokenization, and large-scale data in model training. Karpathy also addresses common challenges like model bias and ethical considerations, emphasizing the importance of fine-tuning models for specific applications. 3. Practical Applications Karpathy highlights how LLMs are transforming various industries, including healthcare, finance, and entertainment. He provides examples of how these models improve services, enhance user experiences, and drive innovation. 4. Clear Explanations Karpathy’s ability to simplify complex topics makes this video accessible to both newcomers and seasoned professionals. His thorough analysis offers valuable insights into the future of artificial intelligence. For those looking to deepen their understanding of LLMs, this video is an invaluable resource. Watch the full video to learn from one of the leading experts in the field: https://lnkd.in/dswuqDhm

𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 Very enlightening paper authored by a team of researchers specializing in computer vision and NLP, this survey underscores that pretraining—while fundamental—only sets the stage for LLM capabilities. The paper then highlights 𝗽𝗼𝘀𝘁-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 (𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴, 𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗮𝗻𝗱 𝘁𝗲𝘀𝘁-𝘁𝗶𝗺𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴) as the real game-changer for aligning LLMs with complex real-world needs. It offers: ◼️ A structured taxonomy of post-training techniques ◼️ Guidance on challenges such as hallucinations, catastrophic forgetting, reward hacking, and ethics ◼️ Future directions in model alignment and scalable adaptation In essence, it’s a playbook for making LLMs truly robust and user-centric. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 𝗕𝗲𝘆𝗼𝗻𝗱 𝗩𝗮𝗻𝗶𝗹𝗹𝗮 𝗠𝗼𝗱𝗲𝗹𝘀 While raw pretrained LLMs capture broad linguistic patterns, they may lack domain expertise or the ability to follow instructions precisely. Targeted fine-tuning methods—like Instruction Tuning and Chain-of-Thought Tuning—unlock more specialized, high-accuracy performance for tasks ranging from creative writing to medical diagnostics. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 The authors show how RL-based methods (e.g., RLHF, DPO, GRPO) turn human or AI feedback into structured reward signals, nudging LLMs toward higher-quality, less toxic, or more logically sound outputs. This structured approach helps mitigate “hallucinations” and ensures models better reflect human values or domain-specific best practices. ⭐ 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 ◾ 𝗥𝗲𝘄𝗮𝗿𝗱 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗜𝘀 𝗞𝗲𝘆: Rather than using absolute numerical scores, ranking-based feedback (e.g., pairwise preferences or partial ordering of responses) often gives LLMs a crisper, more nuanced way to learn from human annotations. Process vs. Outcome Rewards: It’s not just about the final answer; rewarding each step in a chain-of-thought fosters transparency and better “explainability.” ◾ 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗮𝗴𝗲 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴: The paper discusses iterative techniques that combine RL, supervised fine-tuning, and model distillation. This multi-stage approach lets a single strong “teacher” model pass on its refined skills to smaller, more efficient architectures—democratizing advanced capabilities without requiring massive compute. ◾ 𝗣𝘂𝗯𝗹𝗶𝗰 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆: The authors maintain a GitHub repo tracking the rapid developments in LLM post-training—great for staying up-to-date on the latest papers and benchmarks. Source : https://lnkd.in/gTKW4Jdh ☃ To continue getting such interesting Generative AI content/updates : https://lnkd.in/gXHP-9cW #GenAI #LLM #AI RealAIzation

A few weeks ago, we were at Netflix HQ hearing how some of the most thoughtful AI/ML teams are building real-world LLM workflows on Outerbounds. One of the really cool use cases that stuck with me was scaling synopsis generation. (These are the short blurbs that help you decide what to watch on Netflix) And writing millions of them (in the right tone, with the right info, avoiding spoilers) is surprisingly complex. The team walked through how they built a human-in-the-loop pipeline that uses: 🧠 Proprietary + open source LLMs for first drafts ⚙️ Metaflow to orchestrate everything from data prep to prompt chaining to LLM evaluations 🔁 Multiple feedback loops to train and evaluate at scale (editors, writers, even member behavior) LLMs summarize the raw context like scripts, captions, and viewables. Writers then review and edit drafts, giving feedback that’s captured for retraining. And before anything hits production, LLM-as-judge models (running inside a Metaflow flow) score for plot quality, tone, hallucinations, and grammar. Because it’s built on Metaflow, the team can easily swap in new foundation models, adjust prompt strategies, and route output through strict eval layers, all without breaking the system. It’s one of the cleanest examples I’ve seen of how to make LLMs useful and reliable in production. Full Youtube video in the comments 👇enjoy #Netflix #Outerbounds #LLMs #metaflow #genai

This blog unveils the harsh reality behind building ultra-high-quality large language models (LLMs). While many AI practitioners possess deep knowledge of LLM architectures, attention mechanisms, tokenization, and fine-tuning, few succeed in creating production-grade systems that consistently deliver accurate, trustworthy, and user-friendly responses. The article highlights why core technical knowledge is only a fraction of the puzzle, revealing the invisible challenges of data quality, retrieval accuracy, evaluation rigor, and user experience. It introduces a practical Challenge vs. Mitigation blueprint and a Production-Grade LLM Readiness Checklist, concluding with the LLM Quality Pyramid to emphasize that LLM craftsmanship is about data discipline, iteration rigor, and relentless focus on user trust—not just model sophistication.

Excited to share insights from our recent guest tutorial with Rami Krispin's Data Newsletter on building reliable LLMs through high‑quality data, ethical scraping, and robust preprocessing! Here’s what we covered: 1. Why Data Quality Matters • “Garbage in, garbage out”: noisy or biased data will cripple LLM performance and introduce undesired outputs. • Well‑curated datasets (like TinyGSM’s math problems) can help match or exceed much larger models on benchmarks -- this is where companies create value! 2. Defining Data Standards • Source from peer‑reviewed papers, reputable sites, and cross‑verify facts. • Balance breadth (diverse domains) with depth (domain‑specific relevance). • Filter out toxic, spammy, and low‑quality text. • Make sure to respect privacy (GDPR/CCPA), copyright, and Terms of Service. 3. Ethical Web Scraping • Respect robots.txt, rate limits, and ToS. • Avoid PII and sensitive data. • Use polite scrapers (Scrapy, Selenium) or public datasets (Common Crawl) to minimize legal risk. 4. Cleaning & Structuring Data • Strip HTML, normalize text, segment into token‑bounded chunks, and apply keyword filters. • Tools like BeautifulSoup, LangChain, and custom Python scripts streamline cleaning and chunking. 5. Advanced Parsing & Schema‑Driven Extraction • LlamaParse for converting complex PDFs/Word docs into structured Markdown or JSON. • OpenAI Structured Outputs to enforce JSON schemas, ensuring consistent, machine‑readable data. 6. End‑to‑End Pipelines • Combine Scrapy → LlamaParse → OpenAI Structured Outputs → Hugging Face Datasets for scalable, trusted data workflows. Read the full tutorial here: https://lnkd.in/eTsA788w And for hands‑on practice, join our “From Beginner to Advanced LLM Developer” course at Towards AI Academy: https://lnkd.in/eP5NTpDK #AI #MachineLearning #DataEngineering #LLM #DataPrep

Just finished reading an amazing book: AI Engineering by Chip Huyen. Here’s the quickest (and most agile) way to build LLM products: 1. Define your product goals Pick a small, very clear problem to solve (unless you're building a general chatbot). Identify use case and business objectives. Clarify user needs and domain requirements. 2. Select the foundation model Don’t waste time training your own at the start. Evaluate models for domain relevance, task capability, cost, and privacy. Decide on open source vs. proprietary options. 3. Gather and filter data Collect high-quality, relevant data. Remove bias, toxic content, and irrelevant domains. 4. Evaluate baseline model performance Use key metrics: cross-entropy, perplexity, accuracy, semantic similarity. Set up evaluation benchmarks and rubrics. 5. Adapt the model for your task Start with prompt engineering (quick, cost-effective, doesn’t change model weights): craft detailed instructions, provide examples, and specify output formats. Use RAG if your application needs strong grounding and frequently updated factual data: integrate external data sources for richer context. Prompt-tuning isn’t a bad idea either. Still getting hallucinations? Try “abstention”—having the model say “I don’t know” instead of guessing. 6. Fine-tune (only if you have a strong case for it) Train on domain/task-specific data for better performance. Use model distillation for cost-efficient deployment. 7. Implement safety and robustness Protect against prompt injection, jailbreaks, and extraction attacks. Add safety guardrails and monitor for security risks. 8. Build memory and context systems Design short-term and long-term memory (context windows, external databases). Enable continuity across user sessions. 9. Monitor and maintain Continuously track model performance, drift, evaluation metrics, business impact, token usage, etc. Update the model, prompts, and data based on user feedback and changing requirements. Observability is key! 10. Test, Test, Test! Use LLM judges, human-in-the-loop strategies; iterate in small cycles. A/B test in small iterations: see what breaks, patch, and move on. A simple GUI or CLI wrapper is just fine for your MVP. Keep scope under control—LLM products can be tempting to expand, but restraint is crucial! Fastest way: Build an LLM optimized for a single use case first. Once that works, adding new use cases becomes much easier. https://lnkd.in/ghuHNP7t Summary video here -> https://lnkd.in/g6fPsqUR Chip Huyen, #AiEngineering #LLM #GenAI #Oreilly #ContinuousLEarning #ProductManagersinAI