A Practical Guide to Implementing DeepSearch/DeepResearch
jina.ai
A Practical Guide to Implementing DeepSearch/DeepResearch
DeepSeek
deepseek.com
Traditional LLM fine-tuning requires extensive labeled datasets, creating barriers for smaller teams. DeepSeek R1 RL techniques address this by enabling models to fine-tune on smaller, specialized datasets, which are easier for smaller teams to collect. This is especially valuable in domains like math, where outcomes can be automatically verified a
... See moreEvan Armstrong • What Actually Matters (And What Doesn’t) for DeepSeek
The three dynamics above can help us understand DeepSeek's recent releases. About a month ago, DeepSeek released a model called "DeepSeek-V3" that was a pure pretrained model 3 — the first stage described in #3 above. Then last week, they released "R1", which added a second stage. It's not possible to determine everything about these models from th... See more
Dario Amodei • On DeepSeek and Export Controls
Imagine an LLM as a diligent apprentice chef who aspires to become a master chef. To learn the culinary arts, the apprentice starts by reading and studying a vast collection of recipes from around the world. Each recipe represents a piece of text, with various ingredients symbolizing words and phrases. The apprentice’s goal is to understand how to
... See more