Llama Model Results on Advanced AI Tasks

🚀 Exploring Llama-2’s Quality: Can we replace generalist GPT-4 endpoints with specialized OSS models? Dive deep with our technical blogpost to understand the nuances and insights of fine-tuning OSS models. 🔗 Link: https://lnkd.in/gnvZAAG4 We experimented with three unique LLM tasks: 1️⃣ Functional representations from unstructured text 2️⃣ SQL generation 3️⃣ Grade-school math questions Results? Llama-7B fine-tuned impressively for the first two, outperforming GPT-4! But math reasoning? That’s a different ball game with a much bigger gap to bridge. This post isn't just about outcomes. We delve deep into: - Problem formulation 🧠 - Data preparation 📊 - Evaluation setup 🎯 - Setting the right baselines & measuring progress ⏳ Had a blast collaborating with Rehaan Ahmad on this one! All of our experiments were built on top of Ray and Anyscale. Wanna fine-tune Llama-2 on your data? Checkout Anyscale Endpoints: https://lnkd.in/gKsxJJuh 🎸🔥

One of the biggest challenges in deploying LLMs in real workflows is reasoning. Not answering trivia, but actual structured thinking: planning, breaking problems into steps, updating based on intermediate results. LLMs sometimes produce convincing responses that collapse when you inspect the logic. This shows up in math, code generation, question answering, and increasingly, in agentic use cases. Projects fail when models cannot reason reliably over multiple steps. This failure happens because most LLMs are trained to predict the next token, not to reason through a process. They pick the most likely next word, based on patterns they have seen. They are not optimizing for whether each step is logically valid or whether the final result is correct. Even fine-tuned models often reproduce patterns without deeply validating the steps in between. That said, agents have solved this problem to a great extent. A new paper from Stanford and Ceramic AI proposes a surprisingly effective solution: 𝗧𝗵𝗶𝗻𝗸, 𝗣𝗿𝘂𝗻𝗲, 𝗧𝗿𝗮𝗶𝗻. The model generates multiple reasoning paths. Only the ones that lead to correct answers are kept. Then the model is fine-tuned on those filtered traces. This loop is repeated. Over time, the model improves its ability to generate correct, logically coherent solutions, entirely from its own outputs. There is no need for external labels, teacher models, or human ranking. This is a very effective method because pruning to correct final answers is a simple form of reward. Mathematically, the paper shows that this kind of filtered fine-tuning is equivalent to reinforcement learning with a binary reward signal. It avoids the complexity and instability of full RL pipelines but delivers the same benefits. The model learns to prefer better thinking, not just better phrasing. The results are significant!! Gemma-2B improves from 41.9 to 57.6 percent accuracy on GSM8K. Gemma-9B reaches 82 percent, outperforming LLaMA-70B. Even LLaMA-70B improves from 78 to 91 percent, surpassing GPT-4o. 𝗔𝗹𝗹 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗻𝗲𝘄 𝗱𝗮𝘁𝗮 𝗼𝗿 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸. 𝗝𝘂𝘀𝘁 𝘀𝗺𝗮𝗿𝘁 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴. If you’re working on reasoning-heavy tasks, you can try this with open models and modest compute. The steps are simple: 1. Generate several reasoning paths per example 2. Keep only the ones that lead to a correct final answer 3. Fine-tune the model on those filtered examples 4. Repeat with the new model to improve further It works best on tasks with verifiable outcomes like math, code, or structured QA. You need a base model that can already reason somewhat, and a way to check correctness. But you do not need GPT-4, and you do not need human labels. This method pushes us toward a future where models do not just produce good outputs, but learn to produce better reasoning. It is simple, scalable, and grounded in solid learning theory. And it is something teams can start applying today.

📢 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝟮:𝟰 𝗦𝗽𝗮𝗿𝘀𝗲 𝗟𝗹𝗮𝗺𝗮 𝟯.𝟭: 𝗦𝗢𝗧𝗔 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗻 𝗚𝗣𝗨𝘀! Our research team at Neural Magic is redefining AI efficiency with Sparse Llama 3.1 8B—a 50% pruned, 2:4 GPU-compatible sparse model that combines state-of-the-art quantization techniques with unparalleled inference performance. Built using SparseGPT, SquareHead Knowledge Distillation, and a meticulously curated pretraining dataset, Sparse Llama required only 13 billion additional tokens, significantly reducing environmental impact for creating the model and inference while maintaining accuracy. ✨ Key Highlights: • 𝟵𝟴.𝟰% 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘆 on the Open LLM Leaderboard V1 for 𝗳𝗲𝘄-𝘀𝗵𝗼𝘁 tasks. • 𝗙𝘂𝗹𝗹 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘆 (and, in some cases, improved results) in 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 for chat, code generation, and math tasks. • Sparsity alone results in 𝟭.𝟴𝘅 𝗹𝗼𝘄𝗲𝗿 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝟰𝟬% 𝗯𝗲𝘁𝘁𝗲𝗿 𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁; when combined with quantization, it can achieve up to 𝟱𝘅 𝗹𝗼𝘄𝗲𝗿 𝗹𝗮𝘁𝗲𝗻𝗰𝘆. 📊 Dive into our model launch blog for the full story, detailed benchmarks, and links to our open-source models on Hugging Face: https://lnkd.in/eHBZtQqA Together, let’s drive the future of open, efficient AI. Share your thoughts below—how do you see sparsity shaping AI?

UK Health Security Agency: Evaluating Large Language Models for Public Health Classification and Extraction Tasks This report dives into how LLMs can support public health tasks. The team developed public health-specific evals to understand what factors drive variations in LLM performance - the LLM, nature of task, type of text, or specific implementation. Approach: ▪️7 New Annotated Datasets ▪️Existing Datasets and Literature ▪️16 Public Health Specific Eval Tasks ▪️11 Open-weight LLMs & GPT-4 Series Results: ▪️Model: Llama-3.3-70B-Instruct was highest scoring open-weight model. ▪️Task: Significant variability across tasks, with some being more challenging. ▪️Prompting: Few-shot prompting significantly improved performance on challenging tasks. ▪️GPT-Models: GPT-4-Turbo and GPT-4o models performed well, but overall performance was comparable to Llama-3.3-70B-Instruct. ▪️Quantization: Limited impact to performance when with INT-4 quantization Paper Source: https://lnkd.in/eTnEEqgC