I’ve been following the progress of open source language models lately, and one that really caught my attention is DeepSeek. I came across a write up that breaks down how their new R1 model is pushing the bar way higher for open LLMs and honestly, it’s impressive.
What stood out to me wasn’t just the raw benchmarks (which are strong), but the fact that it balances performance with transparency. It’s one thing to have a powerful model, but making it reproducible and open just hits differently in today’s AI space.
The article dives into how R1 competes with some of the big names in both reasoning and multilingual tasks, which is a big deal for devs and researchers outside the usual English dominated frameworks. It also touches on how DeepSeek is prioritizing usability, with strong fine tuning options and documentation, which a lot of open models tend to skip.
If you’re into AI or building on top of LLMs, this model is definitely worth a look. Seeing serious contenders like DeepSeek in the open source world gives me hope for more accessible and collaborative AI going forward.
Top comments (1)
I have run Deepseek-Coder R1 in my local stack to try it out and I was very impressed with the code it came up with versus other models I have tried. I'm running a 12GB 3060 though and I need to find a way to make it generate a bit quicker if I'm going to integrate it any meaningful way. If you know any tips or tricks for performance tuning I'd love to hear them!