Alex Dimakis
@alexdimakis.bsky.social
UC Berkeley Professor working on AI. Co-Director: National AI Institute on the Foundations of Machine Learning (IFML). http://BespokeLabs.ai cofounder
We are releasing OpenThinker-32B, the best 32B reasoning model with open data. We match or outperform Deepseek-R1-32B (a closed data model) in reasoning benchmarks. Congrats to Negin and the whole Open Thoughts team.
github.com/open-thought...
github.com/open-thought...
February 12, 2025 at 8:11 PM
We are releasing OpenThinker-32B, the best 32B reasoning model with open data. We match or outperform Deepseek-R1-32B (a closed data model) in reasoning benchmarks. Congrats to Negin and the whole Open Thoughts team.
github.com/open-thought...
github.com/open-thought...
What if we had the data that DeepSeek-R1 was post-trained on?
We announce Open Thoughts, an effort to create such open reasoning datasets. Using our data we trained Open Thinker 7B an open data model with performance very close to DeepSeekR1-7B distill. (1/n)
We announce Open Thoughts, an effort to create such open reasoning datasets. Using our data we trained Open Thinker 7B an open data model with performance very close to DeepSeekR1-7B distill. (1/n)
January 28, 2025 at 6:23 PM
What if we had the data that DeepSeek-R1 was post-trained on?
We announce Open Thoughts, an effort to create such open reasoning datasets. Using our data we trained Open Thinker 7B an open data model with performance very close to DeepSeekR1-7B distill. (1/n)
We announce Open Thoughts, an effort to create such open reasoning datasets. Using our data we trained Open Thinker 7B an open data model with performance very close to DeepSeekR1-7B distill. (1/n)
The Berkeley Sky computing lab just trained a GPT-o1 level reasoning model, spending only $450 to create the instruction dataset. The data is 17K math and coding problems solved step by step. They created this dataset by prompting QwQ at $450 cost. Q: Impossible without distilling a bigger model?
January 14, 2025 at 6:09 PM
The Berkeley Sky computing lab just trained a GPT-o1 level reasoning model, spending only $450 to create the instruction dataset. The data is 17K math and coding problems solved step by step. They created this dataset by prompting QwQ at $450 cost. Q: Impossible without distilling a bigger model?
AI monoliths vs Unix Philosophy:
The case for small specialized AI models.
Current thinking is that AGI is coming, and one gigantic model will be able to solve everything. Current Agents are mostly prompts on one big model and prompt engineering is used for executing complex processes. (1/n)
The case for small specialized AI models.
Current thinking is that AGI is coming, and one gigantic model will be able to solve everything. Current Agents are mostly prompts on one big model and prompt engineering is used for executing complex processes. (1/n)
January 8, 2025 at 11:28 PM
AI monoliths vs Unix Philosophy:
The case for small specialized AI models.
Current thinking is that AGI is coming, and one gigantic model will be able to solve everything. Current Agents are mostly prompts on one big model and prompt engineering is used for executing complex processes. (1/n)
The case for small specialized AI models.
Current thinking is that AGI is coming, and one gigantic model will be able to solve everything. Current Agents are mostly prompts on one big model and prompt engineering is used for executing complex processes. (1/n)