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starting the week with a true groundbreaking work ๐Ÿ’ฅ Large Language Diffusion Models the first billion-parameter scale diffusion model competitive with its pairs (8B model comparable to LLaMA 3 8B) it gets rid of the michael scott syndrome on existing LLMs

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apolinario ๐ŸŒ

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11,833 Aufrufe โ€ข vor 1 Jahr โ€ขvia X (Twitter)

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this looks insane, LLaDA app just dropped by apolinario ๐ŸŒ A text generation method different from the traditional left-to-right approach a diffusion model with an unprecedented 8B scale, trained entirely from scratch, rivaling LLaMA3 8B in performance
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this looks insane, LLaDA app just dropped by apolinario ๐ŸŒ A text generation method different from the traditional left-to-right approach a diffusion model with an unprecedented 8B scale, trained entirely from scratch, rivaling LLaMA3 8B in performance

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LLaDA (the first Large Language Diffusion Model) is *just* out ๐Ÿ’ฅ and I've built a demo, try out now ๐Ÿ‘จโ€๐Ÿ’ป It's mesmerizing to watch the diffusion process ๐ŸŒ€, and it being a diffusion model gives you superpowers like "the 4th word has to be pineapple" ๐Ÿฆธ Demo and weights ๐Ÿ‘‡
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LLaDA (the first Large Language Diffusion Model) is *just* out ๐Ÿ’ฅ and I've built a demo, try out now ๐Ÿ‘จโ€๐Ÿ’ป It's mesmerizing to watch the diffusion process ๐ŸŒ€, and it being a diffusion model gives you superpowers like "the 4th word has to be pineapple" ๐Ÿฆธ Demo and weights ๐Ÿ‘‡

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Weโ€™re releasing model weights for our 8B- parameter Dynamic Byte Latent Transformer, an alternative to traditional tokenization methods with the potential to redefine the standards for language model efficiency and reliability. Learn more about how Dynamic Byte Latent Transformer is paving the way for groundbreaking developments in the field of language modeling. Read the research paper, and download the model and code.
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Weโ€™re releasing model weights for our 8B- parameter Dynamic Byte Latent Transformer, an alternative to traditional tokenization methods with the potential to redefine the standards for language model efficiency and reliability. Learn more about how Dynamic Byte Latent Transformer is paving the way for groundbreaking developments in the field of language modeling. Read the research paper, and download the model and code.

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1/ Happy to share UniDisc - Unified Multimodal Discrete Diffusion โ€“ We train a 1.5 billion parameter transformer model from scratch on 250 million image/caption pairs using a **discrete diffusion objective**. Our model has all the benefits of diffusion models but now in multimodal space! - flexible compute-quality tradeoff, zero-shot inpainting and editing, better control via classifier-free guidance and lower latency! We open source everything - our code, weights and the training dataset.
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1/ Happy to share UniDisc - Unified Multimodal Discrete Diffusion โ€“ We train a 1.5 billion parameter transformer model from scratch on 250 million image/caption pairs using a **discrete diffusion objective**. Our model has all the benefits of diffusion models but now in multimodal space! - flexible compute-quality tradeoff, zero-shot inpainting and editing, better control via classifier-free guidance and lower latency! We open source everything - our code, weights and the training dataset.

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We are excited to introduce Mercury, the first commercial-grade diffusion large language model (dLLM)! dLLMs push the frontier of intelligence and speed with parallel, coarse-to-fine text generation.
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We are excited to introduce Mercury, the first commercial-grade diffusion large language model (dLLM)! dLLMs push the frontier of intelligence and speed with parallel, coarse-to-fine text generation.

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Introducing DeepThought-8B: Transparent reasoning model built on LLaMA-3.1 with test-time compute scaling. - JSON-structured thought chains & controllable inference paths. - ~16GB VRAM, competitive w/ 70B models. - Open model weights, and inference scripts.
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Introducing DeepThought-8B: Transparent reasoning model built on LLaMA-3.1 with test-time compute scaling. - JSON-structured thought chains & controllable inference paths. - ~16GB VRAM, competitive w/ 70B models. - Open model weights, and inference scripts.

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it's crazy what a 1.5B model can do these days! "VibeThinker-1.5B is a 1.5-billion parameter dense language model. With a total training cost of only $7,800 USD, it achieves reasoning performance comparable to larger models like GPT OSS-20B Medium." runs perfectly on device!
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it's crazy what a 1.5B model can do these days! "VibeThinker-1.5B is a 1.5-billion parameter dense language model. With a total training cost of only $7,800 USD, it achieves reasoning performance comparable to larger models like GPT OSS-20B Medium." runs perfectly on device!

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Apple wasnโ€™t kidding, the iPhone 17 Pro is really built for running LLMs Hereโ€™s LFM2 8B A1B by running on-device with MLX in Locally AI - Local AI Chat, the iPhone runs the 8B model with zero struggle Thanks Prince Canuma for the port to MLX, it made the MLX Swift port possible
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Apple wasnโ€™t kidding, the iPhone 17 Pro is really built for running LLMs Hereโ€™s LFM2 8B A1B by running on-device with MLX in Locally AI - Local AI Chat, the iPhone runs the 8B model with zero struggle Thanks Prince Canuma for the port to MLX, it made the MLX Swift port possible

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The Dream 7B (diffusion reasoning language model) is OUT! ๐Ÿšจ I built a demo so you can test it out (and check the diffusion process live) ๐–ฃฏ๐Ÿ”
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The Dream 7B (diffusion reasoning language model) is OUT! ๐Ÿšจ I built a demo so you can test it out (and check the diffusion process live) ๐–ฃฏ๐Ÿ”

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(1/2) Check out "๐๐จ๐ฅ๐ฒ๐ƒ๐ข๐Ÿ๐Ÿ: Generating 3D Polygonal Meshes with Diffusion Models"! Our model operates directly on the polygons of 3D meshes and generates novel shapes as output through an iterative diffusion process.
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(1/2) Check out "๐๐จ๐ฅ๐ฒ๐ƒ๐ข๐Ÿ๐Ÿ: Generating 3D Polygonal Meshes with Diffusion Models"! Our model operates directly on the polygons of 3D meshes and generates novel shapes as output through an iterative diffusion process.

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Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models "Block diffusion sequentially generates blocks of tokens by performing diffusion within each block and conditioning on previous blocks. By combining strength from autoregressive and diffusion models, block diffusion overcomes the limitations of both approaches by supporting variable-length, higher-quality generation and improving inference efficiency with KV caching and parallel sampling."
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Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models "Block diffusion sequentially generates blocks of tokens by performing diffusion within each block and conditioning on previous blocks. By combining strength from autoregressive and diffusion models, block diffusion overcomes the limitations of both approaches by supporting variable-length, higher-quality generation and improving inference efficiency with KV caching and parallel sampling."

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Major AI breakthrough: Diffusion Large Language Models are here! They're 10x faster and 10x cheaper than traditional LLMs. Here's everything you need to know:

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Switti -- a new scale-wise transformer for text-to-image generation ๐Ÿฆพ ๐Ÿ”ฅ Improved generation of fine-grained details. Outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being up to 7x faster.

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The latent space of earlier generative models like GANS can linearly encode concepts of the data. What if the data was model weights? We present weights2weights, a subspace in diffusion weights that behaves as an interpretable latent space over customized diffusion models.
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The latent space of earlier generative models like GANS can linearly encode concepts of the data. What if the data was model weights? We present weights2weights, a subspace in diffusion weights that behaves as an interpretable latent space over customized diffusion models.

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NVIDIA just released a very impressive text-to-video paper. Video Latent Diffusion Models (Video LDMs) use a diffusion model in a compressed latent space to generate high-resolution videos. Here's a brief overview of how it works: 1. Pre-train image LDM on a dataset of images. 2. Turn the image LDM into a Video LDM by adding temporal layers to model video frames. 3. Fine-tune the Video LDM on encoded video sequences to create a video generator. 4. Temporally align diffusion model upsamplers to generate high-resolution videos. 5. Validate Video LDM on real driving videos of 512x1024 resolution, achieving state-of-the-art performance. 6. Apply the approach in creative content creation with text-to-video modeling. Paper: Project:

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Reaction-diffusion in Blender geometry-nodes. This is the basic "single concentration blur" version. I'm also working on a stable version of the Gray-Scott model. #b3d #geometrynodes #reactiondiffusion
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Reaction-diffusion in Blender geometry-nodes. This is the basic "single concentration blur" version. I'm also working on a stable version of the Gray-Scott model. #b3d #geometrynodes #reactiondiffusion

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๐Ÿš€ Self-speculation brings 6.75x real speedup for LLM generation with SGLang inference! Same model drafts future tokens in Diffusion mode โ†’ then verifies them in AR (causal) mode. One model and one KV cache. Just different attention masks. Thanks to perfect alignment, we get 2ร— longer acceptance lengths than MTP techniques (Eagle-3, MTP, dFlash). We run 2 forward passesโ€ฆ but the 2ร— higher acceptance means we break even - and with zero overhead from extra drafter, KV cache, or LM head that comes with MTP - those are not free. Last week we released Nemotron-Labs-Diffusion + Tri-mode LLMs! We did continued pre-training on Ministral-3 models by switching attention patterns (block causal bidirectional). Result: one model that runs AR mode, Diffusion mode, and Self-Speculation. Diffusion mode already shows high benchmark accuracy - excited to see what happens when someone beats left-to-right acceptance! ๐Ÿ”ฅ Github: Paper: SGLang inference: Try the models on HF:
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๐Ÿš€ Self-speculation brings 6.75x real speedup for LLM generation with SGLang inference! Same model drafts future tokens in Diffusion mode โ†’ then verifies them in AR (causal) mode. One model and one KV cache. Just different attention masks. Thanks to perfect alignment, we get 2ร— longer acceptance lengths than MTP techniques (Eagle-3, MTP, dFlash). We run 2 forward passesโ€ฆ but the 2ร— higher acceptance means we break even - and with zero overhead from extra drafter, KV cache, or LM head that comes with MTP - those are not free. Last week we released Nemotron-Labs-Diffusion + Tri-mode LLMs! We did continued pre-training on Ministral-3 models by switching attention patterns (block causal bidirectional). Result: one model that runs AR mode, Diffusion mode, and Self-Speculation. Diffusion mode already shows high benchmark accuracy - excited to see what happens when someone beats left-to-right acceptance! ๐Ÿ”ฅ Github: Paper: SGLang inference: Try the models on HF:

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All the big language models under one roof for the very first time ๐Ÿคฏ Compare the output of OpenAI ChatGPT, Anthropic's Claude and Cohere's language model in a single playground!! Check out this amazing tool to get the best of large language models ๐Ÿ‘‡
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