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 7 hours ago
EpistemeAI/Audiogemma-TTS
published
a Space
about 9 hours ago
EpistemeAI/Audiogemma-TTS
liked
a Space
about 9 hours ago
Lightricks/ltx-2-distilled
Organizations
replied to
their
post
23 days ago
replied to
their
post
23 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.
replied to
their
post
23 days ago
posted
an
update
23 days ago
Post
242
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
-
- 6 replies
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
KingNish's
post with 👍
over 1 year ago
Post
3634
Mistral Nemo is better than many models in 1st grader level reasoning.
reacted to
davidberenstein1957's
post with 🔥
over 1 year ago
Post
3032
🚀 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
sequelbox's
post with 👍
over 1 year ago
Post
1616
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 :)