Richard Shoemake

🚀 How to Evaluate LLM Applications Large Language Models (LLMs) are powerful, but their real impact depends on how we evaluate them. The right benchmarks and evaluation methods ensure accuracy, fairness, and real-world applicability. 🔍 Key Takeaways from the Framework: ✅ Benchmarks Language Understanding & QA: TruthfulQA, MMLU, DROP Common-sense & Reasoning: ARC, HellaSwag, BIG-Bench Hard Coding: HumanEval, CodeXGLUE Conversation & Chatbots: Chatbot Arena, MT Bench, Stanford HELM ✅ Evaluation Methods Character Level: Levenshtein Distance Word Based: BLEU, ROUGE, METEOR, Perplexity Embedding Based: BERTScore, MoverScore, QAG Score Language Model Based: GPTScore, SelfCheckGPT, G-Eval, Prometheus NLP Models: NLI, BLEURT Frameworks: DeepEval, RAGAS 💡 Whether you’re building chatbots, coding assistants, or reasoning systems, choosing the right benchmark + evaluation method is crucial to measure performance and reliability. 👉 Question for you: Which evaluation method do you think gives the most real-world reflection of LLM performance traditional metrics like BLEU/ROUGE or LLM-based evaluations like GPTScore & SelfCheckGPT? #LLM #AI #MachineLearning #NLP #Evaluation #GenAI #ArtificialIntelligence

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#Quantization in #LLMs: Balancing Efficiency and Accuracy Quantization is a powerful compression technique that reduces model size and memory usage by mapping high-precision values to lower-precision ones. In #largelanguagemodels (LLMs), this means adjusting the precision of weights and activations—making models more efficient but potentially impacting accuracy. This trade-off between memory efficiency and model performance depends heavily on the use case. Moving from formats like FP32 to ultra-low precision ones such as FP4 can deliver significant memory savings but may risk losing task-specific accuracy. With the fifth-generation NVIDIA #Blackwell Tensor Cores, we now have better support for 4-bit formats—enabling both research and deployment at scale. These include: 🔹 FP4 (E2M1): Software-scaled; potential for noticeable accuracy drop vs FP8 🔹 MXFP4: Hardware-scaled with shared power-of-two scale (per 32 values); faster, but still some accuracy trade-offs 🔹 NVFP4: Hardware-scaled with FP8 scale (per 16 values); improved accuracy, especially for larger models These formats reduce memory by up to 25% compared to FP16 and illustrate how NVFP4 builds on prior formats to strike a better balance between performance and precision. Technical blog: https://lnkd.in/dbHYjG7z #LLMs #AI #Quantization #NVIDIA #ModelOptimization #DeepLearning #IndiaAI

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When Shreya and I were figuring out what's the best way to teach advanced AI concepts like 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 RAG, 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐑𝐀𝐆 systems, building RAG that 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤𝐬 in the 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝, 𝐇𝐲𝐃𝐄, etc., we realized we had to address a core industry gap: ...The gap between impressive demos and reliable enterprise solutions. We designed the curriculum backward from the challenges of deploying real-world RAG systems. This means deep dives into robust evaluation, building reliable performance benchmarks, and understanding the trade-offs that dictate success outside a Jupyter Notebook. This focus ensures our cohort members aren't just knowledgeable; they're 𝐝𝐞𝐩𝐥𝐨𝐲𝐚𝐛𝐥𝐞. If you're currently building with RAG, what metric are you using to define a successful system? #ai_classroom

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Meta Llama 4 is powerful and impressive, but here’s something interesting: Early fusion transformers like Llama 4 thrive on massive datasets—think trillions of tokens and enormous computing resources. But what if you’re an enterprise needing multimodal capabilities beyond text, image, and video, without access to those massive datasets and compute? That’s where late fusion transformers step in. Enterprises can use late fusion models to combine modalities of their choosing, training effectively on much smaller datasets and computational budgets. #llama4 #llm #EnterpriseAI

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