TRANSFORMERS: RISE OF THE BEASTS - AMK PRO Series - Shattered Glass Optimus Prime Link: 【Limited to 85 units, available while supplies last.】 Optimus Prime from Transformers: Rise of the Beasts returns in a brand-new Shattered Glass Universe color scheme! Featuring an exclusive metallic paint job and battle-worn weathering, this release brings a fresh take on the dark charisma of an evil leader. Product Name: TRANSFORMERS: RISE OF THE BEASTS – AMK PRO Series Shattered Glass Optimus Prime – Model Kit Figure Height: Approx. 20 cm Materials: ABS, PVC, POM, Die-cast Metal Contents: Optimus Prime figure ×1, Energon base ×1 (with stand) ×1, interchangeable faceplate ×1, interchangeable hands ×6 pairs, Ion blaster ×1, Energon axe ×1, Energon sword ×2, Hand Canon ×2 #YOLOPARK# #shatteredglass# #optimusprime# #ROTB#

swift-transformers 1.4.0 has just been released 🚀 Focus: tokenization correctness, performance, and alignment to transformers. PRs from 15 different contributors, 10 of them new. Thank you! 🙏

Looped Transformers throw away their "work in progress" — the intermediate states from earlier loops. This method puts that discarded signal to use at decode time, almost for free. Title: Decoding Looped Transformers Better for (Almost) Free URL: 📝 Overview LoopCD is a training-free contrastive decoding method for looped Transformers, which gain parameter efficiency by running a shared block through repeated recurrent cycles. No auxiliary model, no retraining required. ❗ Problem it solves Standard decoding discards the intermediate hidden states produced across loop iterations and uses only the final prediction, wasting the naturally aligned "weak-to-strong" prediction pairs that emerge as the loops refine. 🛠️ Method Two variants: LoopCD-Logits contrasts predictions after the output layers (one extra forward pass), while LoopCD-Hidden contrasts hidden states before the coda layers (essentially zero extra cost). Both extrapolate from the first loop toward the final one using a guidance strength ω, with an adaptive version that pushes harder where the model is less confident. 📊 Results Ouro-2.6B-Thinking's AIME 2024 pass@1 jumped from 61.88% to 73.33%, and Huginn's HumanEval climbed from 22.56% to 31.71%. Running at half the loop count with LoopCD still matches or beats the full-depth baseline, cutting forward FLOPs by 22.5–48.2%. 🏢 Use cases Drops directly into existing looped Transformers like Ouro, Huginn, and Parcae, boosting reasoning performance while cutting inference cost with no retraining. #Transformers# #Decoding#

Looped transformers are a popular architecture topic right now. This new technical report extends the loop across tokens. Recurrent Looped Transformer (RLT) makes the decoder recurrent over every token, prompt and response included. A causal encoder builds global KV memory. For each new token, the decoder combines the token's encoder representation with its own final hidden state from the previous token and a sliding-window cache of recent activations. With a 48-layer decoder, the computation path after t tokens runs through 48t decoder blocks, while each token still executes a fixed number of blocks. Depth grows with the sequence and per-token cost stays the same. The same state transition is used for pretraining, SFT, sampling and RL replay, and nothing resets at the prompt-response boundary. RL replay rebuilds states under the current weights instead of reusing stale rollout states. The report is a design proposal. The author states that reasoning gains, hardware speedups and RL scaling are goals that have not been measured yet. Paper: Chat with Paper: