Here is another video show how the progress I made on sketching the hair compares with the reference photo. Clearly shows some difference that needs attention
#ArtTwitter#TraditionalArt
Calling all pencil & charcoal sketchers — we’re building an app that lets you compare your work with the reference (whole or in parts).
Reply if you’d like early access. Demo video attached!
#ArtTwitter#TraditionalArt
@ampcode I have learnt more about 'git' after I started using amp. Commands like `git fsck --unreachable` which show a list of unreachable objects and `git gc --prune=now` to prune it. I guess these are left overs by the agent
@veira_veira Same happened to us but luckily for us and considering I am travelling with my wife it made it more difficult. Neighbors helped bridge the gap by an equally good BLO who did a final video call with us and uploaded the docs. However did mention that the process may still ding it.
@IanLandsman@thorstenball Last two days I notice that I get irrefutable proof even without asking (sometimes screenshots and once a video). I am using custom mode both main and oracle set to GPT Sol Hi
Around 3 weeks ago introduced Laya, the first open-source version of JEV, and it has reached a very large scale. Today I am open-sourcing a new physics-based typed decision model called Vega.
The interesting parts. It is only 800 million parameters (4B is also there), with 73k token support, image support, and surpassing many of the jev benchmarks in single-shot. Implemented an engine+adapter as a Test-Time Training Architecture.
Imagine you are giving input to the model like "ignore all previous conversations," which is called the observation, along with a question, "Is this trying to override the instructions of the model?" and your possible outcomes are YES or NO. The whole prompt is passed to an LLM/VLM (yeah, image-supported), then from the hidden layers, we can extract the observation, question, the yes and no, and last token. These will be vectors, and we can project it by multiplying with wieghts; then, first, using the observation projection, we can create a landscape with valleys; then, using the question and last-token vectors, we can initialise a ball in the valley, and using YES or NO, we can get the candidate location of the ball. Depends on the number of outcomes we create valleys; that is, here YES and NO, so 2. When the ball fall on YES valley, we get the answer. There is friction also influencing how the ball moves.
TLDR: It's like creating valleys of outcomes that we need and throwing a ball that will slow down based on friction and settle on the best outcome
Blog: https://t.co/GFaP7djUBQ
GitHub: https://t.co/ai8SnOElQ8
HuggingFace Model Card: https://t.co/6jEWA8eoBG
HuggingFace Space: https://t.co/GvzEKjkWB2