🏗️ Building on HF

Boning Cui

Bc-AI

AI & ML interests

He/Him. I like LLM's and VLM's. I work with my other friends to make stuff. We are in year 7 and we are enthusiastic about AI. We are based in Australia 🇦🇺 (QLD Btw)

Recent Activity

new activity about 7 hours ago

SolsticeAI/QuATernaryons-demo:Do u have good hardware?

reacted to jiteshkprasad's post with 👍 about 10 hours ago

We just released Drishya Tiny, a 212M-parameter video generator trained from scratch on open-domain video, using the frozen Wan2.2 VAE and the Flan-T5-Base text encoder. It turns a text prompt, or a photo plus a prompt, into a 2-second 256×256 clip. The training run was 15 hours on one L40S, and the model’s own path cost about $21 of GPU time. On VBench (our own sampling and scoring with the official code, not a certified entry) it scores 75.6 on quality, level with VideoCrafter-0.9 (74.9) and close to ModelScope (78.1). On semantic it’s 32.3 against their 65-67, so following the prompt is the weak part. In a controlled test, better captions gave +3.0, and adding 30% photos on top gave about 1.6 more, all of it in what objects are and none in composition. I think at this size, looking real is cheap and understanding the prompt is what costs. It’s a research release, so the weights, code, logs, the full cost ($57.43 with every failed run) and the negative results are all out. Weights are CC BY-NC 4.0 because the training clips are research-only; the code is Apache-2.0. And it runs right in your browser with WebGPU, from text or a photo, on your own device. The browser version uses the tiny TAEW2.2 decoder by Ollin Boer Bohan, so its clips are a bit softer, and a clip takes a few seconds on a recent Mac. Run it in your browser: https://udgamlabs.com/models/drishya-tiny Space: https://huggingface.co/spaces/udgamlabs/Drishya-Tiny Model: https://huggingface.co/udgamlabs/Drishya-Tiny Technical report: https://udgamlabs.com/research/drishya-tiny Write-up: https://udgamlabs.com/blog/drishya-tiny Code: https://github.com/udgamlabs/drishya-tiny

reacted to jiteshkprasad's post with 👀 about 10 hours ago

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$Refract AI Labs's profile picture$

Hello everyone! 👋

A small SmilyAI Labs update!

G1-MINI has now seen around 8B tokens during its current run, and pretraining is still going strong.

Our E1 (Efficiency-1) prototype has also reached 15B pretraining tokens. E1 has 1B total parameters while activating under 100M parameters per token. It features adaptive activation, meaning easier tokens can use less compute while harder tokens receive more.

We plan to open-source E1 ASAP! 🚀

We’re also excited to announce Project Prism, which will provide limited access to our upcoming Orion Flagship model, powered by our T2 architecture.

Note: T2 here refers to the architecture, not our T2 (Thinker-2) model.

Applications for Project Prism are available through the org page, with more details coming soon!

Finally, welcome @soyL061215 , who joined the Hugging Face org today! 🎉

Thanks to our existing members:
@smilyai-large-team @MUK-IS-GOAT @Keeby-smilyai @Bc-AI

— Bc-AI
SmilyAI Labs

Post

306

Hello Everyone!
I am happy to announce a few things.
1. G1-MINI
G1-MINI is now in pretraining and is training at a steady pace. Our current ETAs state completion and launch in about 15-20 days, somewhere near the end of September.
2. G1-NANO
G1-NANO is also being pretrained as we speak at a pace of over 400K tokens per second processing more than 10B tokens in 12 hours. This allows us to train extremely fast, and we will launch it somewhere around 15th September.
3. We have begun work on FrameShot, a dual image and video generation model at around 4B dense parameters. This is expected to launch around late December with no promised date.
- Bc-AI