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Feynman Innovations

ajibawa-2023

AjinkyaBawase

AI & ML interests

LLM, RL, DL, ML, AGI. Developing LLMs (preferably fully fine tuned ) for various use cases.

Recent Activity

reacted to fdaudens's post with 🔥 about 1 month ago

You might not have heard of Moonshot AI — but within 24 hours, their new model Kimi K2 shot to the top of Hugging Face’s trending leaderboard. So… who are they, and why does it matter? Had a lot of fun co-writing this blog post with @xianbao, with key insights translated from Chinese, to unpack how this startup built a model that outperforms GPT-4.1, Claude Opus, and DeepSeek V3 on several major benchmarks. 🧵 A few standout facts: 1. From zero to $3.3B in 18 months: Founded in March 2023, Moonshot is now backed by Alibaba, Tencent, Meituan, and HongShan. 2. A CEO who thinks from the end: Yang Zhilin (31) previously worked at Meta AI, Google Brain, and Carnegie Mellon. His vision? Nothing less than AGI — still a rare ambition among Chinese AI labs. 3. A trillion-parameter model that’s surprisingly efficient: Kimi K2 uses a mixture-of-experts architecture (32B active params per inference) and dominates on coding/math benchmarks. 4. The secret weapon: Muon optimizer: A new training method that doubles efficiency, cuts memory in half, and ran 15.5T tokens with zero failures. Big implications. Most importantly, their move from closed to open source signals a broader shift in China’s AI scene — following Baidu’s pivot. But as Yang puts it: “Users are the only real leaderboard.” 👇 Check out the full post to explore what Kimi K2 can do, how to try it, and why it matters for the future of open-source LLMs: https://huggingface.co/blog/fdaudens/moonshot-ai-kimi-k2-explained

reacted to GeorgiaArm's post with 🔥 about 1 month ago

Join us in Austin tomorrow for AI Camp’s monthly meetup. Arm’s Zach Lasiuk and Geremy Cohen will dive into “From Model to Product: Right-Sizing Infrastructure for Real-World Use Cases.” RSVP here 👉 https://www.aicamp.ai/event/eventdetails/W2025071616

upvoted an article about 1 month ago

Migrating the Hub from Git LFS to Xet

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Hi All, I recently released two Audio datasets which are generated using my earlier released dataset: ajibawa-2023/Children-Stories-Collection

First Audio Dataset:https://huggingface.co/datasets/ajibawa-2023/Audio-Children-Stories-Collection-Large has 5600++ stories in .mp3 format.

Second Audio Dataset:https://huggingface.co/datasets/ajibawa-2023/Audio-Children-Stories-Collection has 600 stories in .mp3 format.

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3681

New Dataset: Software-Architecture
Link: ajibawa-2023/Software-Architecture

I am releasing a Large Dataset covering topics related to Software-Architecture. This dataset consists of around 450,000 lines of data in jsonl.

I have included following topics:

Architectural Frameworks

Architectural Patterns for Reliability

Architectural Patterns for Scalability

Architectural Patterns

Architectural Quality Attributes

Architectural Testing

Architectural Views

Architectural Decision-Making

Advanced Research

Cloud-Based Architectures

Component-Based Architecture

Data Architecture

Emerging Trends

Event-Driven Architecture

Evolvability and Maintainability

Microservices and Monolithic

Microservices Architecture

Security Architecture

Service-Oriented Architecture

Software Design Principles

and Many More!

This dataset is useful in LLM development. Also those who are working on developing Software development related LLMs then this dataset can be useful.

This dataset is very useful to Researchers as well.

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