It uses recursive self improvement techniques. It also improves coding vs others in Humaneval.
Thomas Yiu PRO
legolasyiu
AI & ML interests
Superalignment , alignment, safety LLM, AI, AGI, ASI.
Donation/Funding
https://account.venmo.com/u/Thomas-Yiu
Recent Activity
updated
a Space
about 8 hours ago
EpistemeAI/README
updated
a model
2 days ago
EpistemeAI/RSI-AI-V1.0
published
a model
2 days ago
EpistemeAI/RSI-AI-V1.0
Organizations
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19 days ago
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20 days ago
Codeforce-GPT-oss-20b leads the benchmark, surpassing even larger models like Qwen 3 235B and DeepSeek-R1 70B. Its superior reasoning and code synthesis capabilities indicate an optimized training strategy rather than sheer scale dominance.
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We release open-weight early experimental Codeforce metatune-gpt20b, fine tuned version of OpenAI's gpt-oss-20b model, this is one of the first public release recursive self improving AI.
EpistemeAI/Codeforce-metatune-gpt20b
EpistemeAI/Codeforce-metatune-gpt20b
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Episteme released a Vibe Coding LLM with 20B params based on GPT OSS 20B, it is easier to Vibe Code. It is preview right now.
EpistemeAI/VibeCoder-20B-alpha
Did you get a chance to try it?
Hello Belo, nice to meet you, I am special fine tune models.
I have cool model, from EpistemeAI,
https://huggingface.co/EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto
reacted to
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Mistral Nemo is better than many models in 1st grader level reasoning.
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🚀 We will be generating a preference dataset for DPO/ORPO and cleaning it with AI feedback during our upcoming meetup!
In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.
This session is perfect for you
- if you’re getting started with distilabel or synthetic data
- if you want to learn how to use LLM inference endpoints for **free**
- if you want to discover new functionalities
- if you want to provide us with new feedback
Sign up here: https://lu.ma/dt0c7jru
In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.
This session is perfect for you
- if you’re getting started with distilabel or synthetic data
- if you want to learn how to use LLM inference endpoints for **free**
- if you want to discover new functionalities
- if you want to provide us with new feedback
Sign up here: https://lu.ma/dt0c7jru
reacted to
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NEW math-instruct model + dataset!
ValiantLabs/Llama3.1-8B-Cobalt is our new math-instruct model.
Trained using a synthetic math-instruct dataset generated with Llama 3.1 405b. Find the dataset here: sequelbox/Polytope
More to come soon :)
ValiantLabs/Llama3.1-8B-Cobalt is our new math-instruct model.
Trained using a synthetic math-instruct dataset generated with Llama 3.1 405b. Find the dataset here: sequelbox/Polytope
More to come soon :)