Strategies for Optimizing Models

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.

🚀 New Paper Alert! Excited to share our latest paper, "Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications", now available on arXiv - https://lnkd.in/dZrUEGqD! Large Language Models (LLMs) have unlocked incredible capabilities across AI applications, from search and recommendations to generative tasks. However, their sheer size and computational cost often make them impractical for real-world deployment at scale. In this work, we explore techniques to train and deploy Small Language Models (SLMs) that retain much of the power of their larger counterparts while being significantly more efficient. 🔍 Key Contributions: ✅ Knowledge Distillation – We efficiently transfer knowledge from large models to smaller ones, ensuring strong task performance. We demonstrate the effectiveness of various flavors of distillation - on-policy, supervised, seqKD ✅ Model Compression (Pruning & Quantization) – We apply structured pruning (OSSCAR) and quantization (GPTQ, QuantEase, FP8) to drastically reduce model size while maintaining accuracy. ✅ Real-World Deployment at LinkedIn – We showcase how we deploy SLMs for ranking, recommendation, and reasoning tasks at LinkedIn, achieving 20× model compression with minimal accuracy loss. ✅ Serving Optimizations – We detail inference speedups, leveraging techniques like RadixAttention, FlashInfer, and tensor parallelism on NVIDIA H100 GPUs to optimize latency and throughput. Key Results: 📉 20× reduction in model size with minimal accuracy loss ⚡ 40% improvement in attention latency through structured pruning 🚀 Significant serving speedup with FP8 quantization and prefix caching This work is a step toward making LLMs more efficient, scalable, and production-ready for industry use cases. We hope it helps others looking to deploy high-performance AI at scale! A huge shoutout to my incredible co-authors at LinkedIn for their contributions - Qingquan Song, Kayhan Behdin, Yun Dai, Ata Fatahi, Shao Tang, HEJIAN SANG, Gregory Dexter, Sirou Z., Jason (Siyu) Zhu, Tejas Dharamsi, Maziar Sanjabi, Vignesh Kothapalli, Hamed Firooz, Zhoutong Fu, Yihan Cao, Pin-Lun (Byron) Hsu, Fedor Borisyuk, Zhipeng(Jason) Wang, PhD, Rahul Mazumder, Natesh Pillai, Luke Simon Special thanks to our leadership Zhipeng(Jason) Wang, PhD, Xiaobing Xue, Necip Fazil Ayan, Deepak Agarwal for empowering us and helping us push the envelope! #AI #MachineLearning #LLMs #Efficiency #AIatScale #DeepLearning #KnowledgeDistillation #Pruning #Quantization #Deployment

We’re not yet at the point where a single LLM call can solve many of the most valuable problems in production. As a consequence, practitioners frequently deploy *compound AI systems* composed of multiple prompts, sub-stages, and often with multiple calls per stage. These systems' implementations may also encompass multiple models and providers. These *networks-of-networks* (NONs) or "multi-stage pipelines" can be difficult to optimize and tune in a principled manner. There are numerous levels at which they can be tuned, including but not limited to: (I) optimizing the prompts in the system (see [DSPy](https://lnkd.in/g3vcqw3H) (II) optimizing the weights of a verifier or router (see [FrugalGPT](https://lnkd.in/g36kfhs9)) (III) optimizing the architecture of the NON (see [NON](https://lnkd.in/g5tvASaz) and [Are More LLM Calls All You Need](https://lnkd.in/gh_v5b2D)) (IV) optimizing the selection amongst and composition of frozen modules in the system (see our new work, [LLMSelector](https://lnkd.in/gkt7nj8w)). In a multi-stage compound system, which LLM should be used for which calls, given the spikes and affinities across models? How much can we push the performance frontier by tuning this? Quite dramatically → in LLMSelector, we demonstrate performance gains from *5-70%* above that of the best mono-model system across myriad tasks, ranging from LiveCodeBench to FEVER. One core technical challenge is that the search space for optimizing LLM selection is exponential. We find, though, that optimization is still feasible and tractable given that (a) the compound system's aggregate performance is often *monotonic* in the performance of individual modules, allowing for greedy optimization at times, and (b) we can *learn to predict* module performance This is an exciting direction for future research! Great collaboration with Lingjiao Chen, Boris Hanin, Peter Bailis, Matei Zaharia, James Zou, and Ion Stoica! References: LLMSelector: https://lnkd.in/gkt7nj8w Other works → DSPy: https://lnkd.in/g3vcqw3H FrugalGPT: https://lnkd.in/g36kfhs9) Networks of Networks (NON): https://lnkd.in/g5tvASaz Are More LLM Calls All You Need: https://lnkd.in/gh_v5b2D

TL;DR 🧠 Smaller LLMs outperform giants: A 1B LLM can surpass a 405B LLM on reasoning tasks like MATH-500 using compute-optimal Test-Time Scaling (TTS). 🚀 Efficiency boost: Smaller models achieve higher accuracy with 14.1× faster inference and 256× fewer FLOPS compared to larger models. 🔍 Key insight: TTS strategies depend on policy model size, Process Reward Models (PRMs), and problem difficulty. Problems & Solutions 🛑 Problem 1: Lack of systematic analysis of how policy models, PRMs, and problem difficulty affect TTS. ✅ Solution: Introduced reward-aware compute-optimal TTS to dynamically adapt strategies. 🛑 Problem 2: PRMs struggled with out-of-distribution (OOD) responses and token-length bias. ✅ Solution: Implemented absolute difficulty thresholds and PRM-Vote aggregation to improve robustness. Experiments & Setup 📚 Tasks: MATH-500 (500 problems) and AIME24 (advanced math challenges). 🤖 Models: Llama 3 (1B-405B), Qwen2.5 (0.5B-72B), and DeepSeek-R1 variants. ⚖️ Metrics: Pass@k, token efficiency, FLOPS comparison. 🔧 Ablations: PRM scoring methods (Min/Last/Avg) and voting strategies (Majority/PRM-Max/PRM-Vote). 💻 Hardware: 8×A100 GPU clusters for TTS experiments with beam width=4 and max tokens=8192. Novel Insights 🧩 Policy model size matters: Best-of-N (BoN) works well for large models, while Beam Search and DVTS excel for smaller ones. 📉 PRM limitations: Observed over-criticism, error neglect, and token-length bias in PRMs, impacting TTS performance. ⚖️ Trade-off: TTS gains diminish as policy model size increases (e.g., 154.6% gain for 1B vs. 9.5% for 72B). Improvements Over Prior Work 🚀 135× size gap: A 3B model outperforms a 405B model, improving the prior benchmark of 23×. 🔬 Enhanced PRMs: Qwen2.5-Math-PRM-72B enables 7B models to surpass o1 and DeepSeek-R1. ⏱️ Efficiency: 1B model + TTS achieves 256× fewer FLOPS compared to 405B CoT models. Key Implementation Details 🔄 Reward-aware TTS: Integrated PRM scores into a Markov Decision Process (MDP) framework for dynamic scaling. 🌳 DVTS: Parallel subtree exploration for diverse reasoning paths. 📉 Absolute difficulty bins: Replaced quantile-based thresholds with fixed Pass@1 ranges (easy: 50%-100%, medium: 10%-50%, hard: 0%-10%). Resources Paper: Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling (https://lnkd.in/g55ybikb) 🤖 Models: Llama-3.2-3B-Instruct (https://lnkd.in/gnQ3d87S), Qwen2.5-Math-PRM (https://lnkd.in/gk6gMqMw). 🔧 Framework: OpenR (https://lnkd.in/gCPxPR4H) for TTS pipelines. 📊 Datasets: MATH-500 (https://lnkd.in/g4jvAzsp), PRM800K (https://lnkd.in/gEb6XE3A). 🌐 Project Page: Compute-Optimal TTS (https://lnkd.in/gVutpamZ).

🎯 How can we use a low-fidelity optimization model to achieve similar performance to a high-fidelity model? Many decision-making algorithms can be viewed as tuning a low-fidelity model within a high-fidelity simulator to achieve improved performance. A great example comes from Cost Function Approximations (CFAs) by Warren Powell. CFAs embed tunable parameters, such as cost coefficients, into a simplified, deterministic model. These parameters are then refined by optimizing performance in a high-fidelity stochastic simulator, either via derivative-free or gradient-based methods. A similar philosophy appears in optimal control, where controllers are tuned using simulation optimization. ⚙️ Inspired by this paradigm, my student Asha Ramanujam recently developed the PAMSO algorithm. PAMSO—Parametric Autotuning for Multi-Timescale Optimization—tackles complex systems that operate across multiple timescales: High-level decision layer: makes strategic decisions (e.g., planning, design). Low-level decision layer: takes high-level inputs, makes detailed operating decisions (e.g., scheduling), applies detailed constraints and uncertainties, and computes the true objective. However, one-way top-down communication between layers often results in infeasibility or poor solutions due to mismatches between the high-level and the detailed low-level operating models. 💡 PAMSO augments the high-level model with tunable parameters that serve as a proxy for the complex physics and uncertainties embedded in the low-level model. Instead of attempting to jointly solve both levels, we fix the hierarchical structure: the high-level layer makes planning or design decisions, and then passes them down to the low-level scheduling or operational layer, which acts as a high-fidelity simulator. We treat this top-down hierarchy as a black box: The inputs are the tunable parameters embedded in the high-level model. The output is the overall objective value after the low-level simulator evaluates feasibility and performance. By optimizing these parameters using derivative-free methods, PAMSO is able to steer the entire system toward high-quality, feasible solutions. 🚀 Bonus: Transfer Learning! If these parameters are designed to be problem-size invariant, they can be tuned on smaller problem instances and transferred to solve larger-scale problems with minimal extra effort. ⚙️ Case studies demonstrate PAMSO’s scalability and effectiveness in generating good, feasible solutions: ✅ A MINLP model for integrated design and scheduling in a resource-task network with ~67,000 variables ✅ A massive MILP model for integrated planning and scheduling of electrified chemical plants and renewable energy with ~26 million variables Even solving the LP relaxation of these problems is beyond memory limits, and their structure is not easily decomposable for optimization techniques. https://lnkd.in/gDfcvDaZ

With so many options for building AI systems based on LLMs, I found Databricks' guide by Jonathan Frankle to be a helpful resource covering when and how to apply different methods, including Prompt Engineering, In-Context Learning, Retrieval-Augmented Generation (RAG), Fine-Tuning, and Pre-Training. 1. Prompt Engineering (Including In-Context Learning) Involves crafting and structuring input prompts to guide a model’s output. This includes providing examples within the prompt (in-context learning) to influence how the model generates responses. Pros: - No need for modifying the model. - Quick to implement and cost-effective. - Flexible, can include providing examples to improve the model’s understanding. Cons: - Limited control over output quality, especially for specialized tasks. - Requires expertise in creating effective prompts and examples. - Performance improvements may be limited compared to fine-tuning. Use Case: Suitable for quickly adapting a model to new tasks or obtaining better results without additional training, especially when providing relevant examples within the prompt. 2. Retrieval-Augmented Generation (RAG) Combines the model's responses with relevant external data retrieved from a database to provide more accurate and contextually relevant answers. Pros: - Enhances the model’s responses by incorporating up-to-date or domain-specific information. - Cost-effective compared to training or fine-tuning. - Versatile and can be combined with other techniques like fine-tuning. Cons: - The quality of the output depends on the relevance of the retrieved data. - More complex to implement due to the need for a reliable retrieval system. Use Case: Best when specific, accurate, and context-rich responses are needed. 3. Fine-Tuning Adjusting a pre-trained model’s parameters by training it on a specific, smaller dataset to tailor it to a particular task or domain. Pros: - Highly customizable for specific tasks. - Can significantly improve the model’s accuracy on specialized tasks. Cons: - Resource-intensive and time-consuming. - Risk of overfitting, leading to a model that may not generalize well. Use Case: Suitable for scenarios requiring high accuracy in a specialized domain, where the investment in additional training is justified. 4. Pre-Training Training a model from scratch or continuing the training of a model on a large dataset to provide a strong foundational understanding before fine-tuning. Pros: - Provides control over the model’s foundational knowledge. - Allows the creation of highly specialized models tailored to specific needs. Cons: - Extremely resource-intensive and time-consuming. - Requires extensive datasets and computational power. Use Case: Best when a highly specialized model is needed, or when existing models do not meet the required criteria, and there are sufficient resources to build a model from the ground up. https://lnkd.in/gbF_3e_F

Here's how to master fine-tuning LLMs from basics to breakthroughs: 1/ Start with NLP basics. Everything else builds on this. 2/ Choose the right fine-tuning method based on your goal: task vs. domain. 3/ Use PEFT to save compute. It’s faster, cheaper, and just as good. 4/ LoRA lets you fine-tune big models with tiny updates. 5/ QLoRA takes it further, 4-bit weights without losing performance. 6/ DoRA adds structure to QLoRA for better compression. 7/ Adapters help plug new knowledge into frozen models. 8/ Multiple adapters let one model switch between tasks. 9/ Half Fine-Tuning gives you LoRA-level results with less hassle. 10/ LaMini optimizes memory while keeping performance intact. 11/ Mixture of Experts splits work between specialist models. 12/ Mixtral 8x7B is the current benchmark for expert-based scaling. 13/ Mixture of Agents uses agent collaboration, like MoE, but smarter. 14/ PPO fine-tunes LLMs using reward signals (think trial and error). 15/ DPO skips the reward model, directly optimizing for user preference. 16/ DPO vs. PPO? Use DPO for faster, cleaner alignment. 17/ Tutorials are included for both, no guesswork needed. 18/ ORPO improves speed by pruning dead weight in models. 19/ Knowing when to prune is just as key as knowing what to train. 20/ RAG isn’t a fine-tuning method. Use it before fine-tuning for best results. 21/ Fine-tuning without RAG is like writing without research. 22/ Combine RAG with LoRA to keep models fresh and informed. 23/ Use Hugging Face’s PEFT tools to skip the setup mess. 24/ LoraConfig and BitsAndBytesConfig make fine-tuning plug-and-play. 25/ Templates are available, don’t start from scratch. 26/ Don’t fine-tune everything. Target what changes, not what works. 27/ Avoid full-model updates unless absolutely needed. 28/ Aligning models is as much art as it is science. 29/ If you’re lost, follow the seven-stage pipeline in the guide. 30/ This paper is your north star if you care about efficient AI. Share this if you’ve ever asked, “Where’s the actual fine-tuning playbook?”

LLM Cost Optimization Strategies: Achieving Efficient AI Workflows Large Language Models (LLMs) are transforming industries but come with high computational costs. To make AI solutions more scalable and efficient, it's essential to adopt smart cost optimization strategies. 🔑 Key Strategies: 1️⃣ Input Optimization: Refine prompts and prune unnecessary context. 2️⃣ Model Selection: Choose the right-size models for task-specific needs. 3️⃣ Distributed Processing: Improve performance with distributed inference and load balancing. 4️⃣ Model Optimization: Implement quantization and pruning techniques to reduce computational requirements. 5️⃣ Caching Strategy: Use response and embedding caching for faster results. 6️⃣ Output Management: Optimize token limits and enable stream processing. 7️⃣ System Architecture: Enhance efficiency with batch processing and request optimization. By adopting these strategies, organizations can unlock the full potential of LLMs while keeping operational expenses under control. How is your organization managing LLM costs? Let's discuss!

How to Lower LLM Costs for Scalable GenAI Applications Knowing how to optimize LLM costs is becoming a critical skill for deploying GenAI at scale. While many focus on raw model performance, the real game-changer lies in making tradeoffs that align with both technical feasibility and business objectives. The best developers don’t just fine-tune models—they drive leadership alignment by balancing cost, latency, and accuracy for their specific use cases. Here’s a quick overview of key techniques to optimize LLM costs: ✅ Model Selection & Optimization • Choose smaller, domain-specific models over general-purpose ones. • Use distillation, quantization, and pruning to reduce inference costs. ✅ Efficient Prompt Engineering • Trim unnecessary tokens to reduce token-based costs. • Use retrieval-augmented generation (RAG) to minimize context length. ✅ Hybrid Architectures • Use open-source LLMs for internal queries and API-based LLMs for complex cases. • Deploy caching strategies to avoid redundant requests. ✅ Fine-Tuning vs. Embeddings • Instead of expensive fine-tuning, leverage embeddings + vector databases for contextual responses. • Explore LoRA (Low-Rank Adaptation) to fine-tune efficiently. ✅ Cost-Aware API Usage • Optimize API calls with batch processing and rate limits. • Experiment with different temperature settings to balance creativity and cost. Which of these techniques (or a combination) have you successfully deployed to production? Let’s discuss! CC: Bhavishya Pandit #GenAI #Technology #ArtificialIntelligence

📻 Tune LLMs for new tasks ➢ Given the plethora of LLMs available today, from OpenAI’s ChatGPT to Meta's Llama, an important question that arises is: how can we best leverage these powerful models for new tasks beyond what they were originally trained on? ➢ There are several techniques that are commonly leveraged: 1. Finetuning: which modifies the entire model using new task data, demanding more resources but fully adapting the model. 2. Retrieval augmentation: which fetches relevant knowledge to guide the LLM, reducing hallucinations without training. 3. Prompt engineering: which shapes prompts to direct the model's response, quick but requires iterations. 4. Parameter-efficient tuning: which adjusts only a few parameters, avoiding catastrophic forgetting. ➢ The best technique depends on the specific use case, resources, and the balance between performance and efficiency. ➢ In this article, http://LLMtune.vinija.ai, Aman and I share a deep dive into each of these methodologies, when to use which, and their benefits! Please feel free to reach out to us for any comments or suggestions!