If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/X8z3SMqptRAvi Chawla (@_avichawla):If you need a video guide to Karpathy's nanochat, check out Stanford's CS336!It covers:- Tokenization- Resource Accounting- Pretraining- Finetuning (SFT/RLHF)- Overview of Key Architectures- Working with GPUs- Kernels and Tritons- Parallelism- Scaling Laws- Inference https://t.co/7oCl2Od1fO ...

由于没有提供具体报告内容，我无法进行总结和分析，只能根据你提供的框架进行模拟输出。为了更好地完成任务，请提供报告的具体内容。 Financial Performance (Example) - Company revenue increased by 15% year-over-year [1] - Gross margin reached 45%, a 2% increase from the previous year [1] Market Trends (Example) - The industry is experiencing a shift towards sustainable practices [1] - Increased demand for electric vehicles is driving growth in the battery sector [1] Investment Opportunities (Example) - Emerging markets present significant growth potential for the company [1] - Investment in renewable energy infrastructure is expected to yield high returns [1] Potential Risks (Example) - Regulatory changes could impact the company's operations [1] - Increased competition may lead to price erosion [1]

Educational Resources - Stanford's CS336 video guide covers topics essential for Frontier AI Labs jobs [1] - The curriculum includes tokenization, resource accounting, pretraining, and finetuning (SFT/RLHF) [1] - Key AI architectures, GPU usage, kernels, parallelism, and scaling laws are addressed [1] AI Development Lifecycle - The guide also covers inference, evaluation, and alignment in AI models [1]

Model Training & Context Expansion - Fine-tuning on longer documents with 128K context is an insufficient response in a Research Scientist interview at OpenAI [1] - The question focuses on expanding the context length of an LLM from 2K to 128K tokens [1]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/Y6ooICWVnOAvi Chawla (@_avichawla):You're in a Research Scientist interview at OpenAI.The interviewer asks:"How would you expand the context length of an LLM from 2K to 128K tokens?"You: "I will fine-tune the model on longer docs with 128K context."Interview over.Here's what you missed: ...

At 128K context, prefilling costs drop from ~$0.65 to ~$0.35 per million tokens. And Decoding drops from ~$2.4 to ~$0.8.And the performance stays the same. On some long-context benchmarks, V3.2 actually scores higher.Sparse attention isn’t new. But making it work without losing quality is hard.What are some other techniques to increase the context lengths of LLMs? ...

3) DeepSeek Sparse Attention (DSA)DeepSeek’s new V3.2 model introduces DeepSeek Sparse Attention (DSA), which brings complexity down from O(L²) to O(Lk), where k is fixed.How it works:A lightweight Lightning Indexer scores which tokens actually matter for each query.Small number of heads, runs in FP8, computationally cheap.Then a selection mechanism retrieves only the top-k key-value entries.The key insight is that only 2048 tokens get selected per query, regardless of context length.So the expensive attent ...

You're in a Research Scientist interview at OpenAI.The interviewer asks:"How would you expand the context length of an LLM from 2K to 128K tokens?"You: "I will fine-tune the model on longer docs with 128K context."Interview over.Here's what you missed: ...

RT Avi Chawla (@_avichawla)Docker explained in 2 minutes!Most developers use Docker daily without understanding what happens under the hood. Here's everything you need to know.Docker has 3 main components:1️⃣ Docker Client: Where you type commands that talk to the Docker daemon via API.2️⃣ Docker Host: The daemon runs here, handling all the heavy lifting (building images, running containers, and managing resources)3️⃣ Docker Registry: Stores Docker images. Docker Hub is public, but companies run private reg ...

Docker explained in 2 minutes!Most developers use Docker daily without understanding what happens under the hood. Here's everything you need to know.Docker has 3 main components:1️⃣ Docker Client: Where you type commands that talk to the Docker daemon via API.2️⃣ Docker Host: The daemon runs here, handling all the heavy lifting (building images, running containers, and managing resources)3️⃣ Docker Registry: Stores Docker images. Docker Hub is public, but companies run private registries.Here's what happens ...