Maybe I missed something or just forgot, but I remember right this is the first time I've seen data from @internetarchive mentioned in court docs filed for the case. A lot of attention has been on Libgen, but downloading Internet Archive for Llama-training seems noteworthy.
LLMS
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MCP Protocol Upgrades and Authentication Support Coming Soon
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I'm very excited about all the upgrades that are about to land as well. We recently added auth support to the spec and there's much more to come very soon. I wouldn't be surprised if MCP turns out to be one of the biggest things to happen to LLM app development in 2025.
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O1 Replication with Minimal Training Data: Strategic Progress Report
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Hmm. For even more "Less is more", check out the "O1 Replication Journey: A Strategic Progress Report — Part 1" paper from 2024: https://
arxiv.org/abs/2410.18982 . Only 327 training samples -
Wait as New Prompt Engineering Technique Replacing Step by Step
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"Wait." is the new "Think step by step"
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S1 Simple Test-Time Scaling: Improving Reasoning Models with SFT
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Following up on my reasoning model article, I just read the new "s1: Simple Test-Time Scaling" paper, which describes an interesting method for improving reasoning models using a combination of pure supervised finetuning (SFT) and scaling inference compute.
In short, their -
Reasoning LLMs: Paper, Article, and S1 GitHub Repository
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– Link to the paper: https://
arxiv.org/abs/2501.19393
– My reasoning LLM article (they use methods 1 and 4): https://
magazine.sebastianraschka.com/p/understandin
g-reasoning-llms
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– The s1 GitHub repo: https://
github.com/simplescaling/
s1
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Using Ollama for Testing and vLLM for Production Serving
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I use ollama most of the times for testing and prototyping and then vLLM when i need to serve it.
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Performance evaluation of reasoning models on agentic data science tasks
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Adyen's new Data Agents Benchmark shows that DeepSeek-R1 struggles on data science task! How well do reasoning models perform on agentic tasks? Until now, all indicators seemed to show that they worked really well. On our recent reproduction of Deep Search, OpenAI's o1 was
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Thinking Time Improves AI Model Accuracy
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7/ Thinking Time Improves Accuracy Research on deepseek-R1 confirms:
Longer response times = More accurate and insightful answers Encourage it with prompts like:
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Prompting strategies for DeepSeek-R1
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8/ Conclusion Deepseek-R1 is powerful, but it requires the right prompting strategies. Master these techniques and you’ll get far more intelligent responses. Try them and let me know how it goes!