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Today we release LFM2.5-Encoder-230M and LFM2.5-Encoder-350M: bidirectional encoders that stay fast at long context, even on CPU. > LFM2.5-Encoder-230M: about 3.7x faster than ModernBERT-base on CPU at 8,192 tokens. Under 30s per forward pass, versus over a minute and a half. > LFM2.5-Encoder-350M: 4th of 14 models on GLUE,... show more

Liquid AI

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198,990 görüntüleme • 1 ay önce •via X (Twitter)

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Two sizes in the same family: > LFM2.5-Encoder-350M: the higher-accuracy pick. > LFM2.5-Encoder-230M: smaller and faster, for tighter hardware or higher throughput. Both are pre-trained with a masked-language objective, so you fine-tune them for classification, token-level tasks, or retrieval. (2/6)

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Liquid AI1 ay önce

Built on the LFM2 hybrid backbone, adapted from causal decoders into bidirectional encoders: bidirectional attention, non-causal short convolutions, and a 30% mask rate. The backbone's cost grows slowly as inputs get longer. > Context: 8,192 tokens, about 13 to 15 pages in one forward pass. > Runs on CPU with no GPU, where the speed edge is largest. > On GPU, the same pattern holds at the longest inputs. (3/6)

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Liquid AI1 ay önce

See it in action. We built demos that run entirely on CPU: > Prompt routing: > Policy linting: > Spell checker: Bonus: we even run the encoder as a masked-diffusion chatbot. (4/6)

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LFM2.5-Encoder-230M and LFM2.5-Encoder-350M are available now. > LFM2.5-Encoder-230M: > LFM2.5-Encoder-350M: > Blog: > Docs:

HT - Galactic President Superstar McAwesomeville profil fotoğrafı

HT - Galactic President Superstar McAwesomeville1 ay önce

Will there be ONNX weights released? Can't wait to run it on the edge!

Maksim Kuznetsov profil fotoğrafı

Maksim Kuznetsov1 ay önce

Great news! Excited to try it out. LFM2-2.6B was a perfect base model for our C3LM!

Pau Labarta Bajo profil fotoğrafı

Pau Labarta Bajo1 ay önce

Great job Viviana ❤️

Jaya Nayak profil fotoğrafı

Jaya Nayak1 ay önce

Sub-30 second forward passes on a CPU at 8k tokens is absolute magic. 😊

AX⚡ profil fotoğrafı

AX⚡1 ay önce

952.4 t/s 0.6gb vram for 350M

Vantix AI Agency profil fotoğrafı

Vantix AI Agency1 ay önce

LFM2 5 Encoders deliver long context speed on CPU

Axly profil fotoğrafı

Axly1 ay önce

LiquidAI just makes a great model. I recently built an iOS app, that runs the LFM 8b a1b at 50 tokens per second. Crazy, right?

Neeraj Kumar profil fotoğrafı

Neeraj Kumar1 ay önce

Fast long context on CPUs could quietly unlock many practical deployments. Impressive work.

Hussein Kizz ★ profil fotoğrafı

Hussein Kizz ★1 ay önce

You guys are doing the most important work right now, and making AI applicable and useful, thanks so much!

Jacob Portes profil fotoğrafı

Jacob Portes1 ay önce

awesome! @zach_nussbaum

Gregor profil fotoğrafı

Gregor1 ay önce

the under 30s figure, what batch size is that at? single sample vs batched throughput is a totally different story for anything doing live inference at scale

luis profil fotoğrafı

luis1 ay önce

@Presidentlin

Melvin Vivas profil fotoğrafı

Melvin Vivas1 ay önce

wow, this is awesome

Ege Sabanci profil fotoğrafı

Ege Sabanci1 ay önce

incredible work! we are deeply admiring the open source contributions of @liquidai as @koniclabs keep up team 🫡

paolo profil fotoğrafı

paolo1 ay önce

Great job, I am looking for a simple practical application in office wotk

Mykhailo Sorochuk profil fotoğrafı

Mykhailo Sorochuk1 ay önce

8k token context on CPU at that speed is a game changer for lightweight pipelines

AI Mastery Guide profil fotoğrafı

AI Mastery Guide1 ay önce

Beating a model 10x its size on GLUE is a huge efficiency win.

bnw profil fotoğrafı

bnw1 ay önce

question: when would I use this over something like sentence-BERT? for larger documents and if I want it on CPU?

Sebastian Buzdugan profil fotoğrafı

Sebastian Buzdugan1 ay önce

cpu wins at batch size 1 often disappear once batching enters production

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GANGA TODAY1 ay önce

Generative Reasoning Protocol (GRP) v0.6

Andrii profil fotoğrafı

Andrii1 ay önce

4th of 14 on GLUE/SuperGLUE while being roughly a third the size of most competitors is a legitimate benchmark result, but the more interesting number is the 3.7x CPU speedup at 8,192 tokens — that's the one that actually determines whether prompt routing runs cheaply enough to sit in front of every request instead of being a nice-to-have.

Aivan Monceller profil fotoğrafı

Aivan Monceller1 ay önce

Are you planning to publish the training code for the t the zero shot dual tower head, and an official ONNX

Eplurubusnullus profil fotoğrafı

Eplurubusnullus1 ay önce

You guys beat me to it. This is what I've been trying to train for meept (

Mauro S. profil fotoğrafı

Mauro S.1 ay önce

great job!👏

toriset profil fotoğrafı

toriset1 ay önce

looks pretty cool, but is this worth it to use for LLM routing? by like choosing the subset that a task is in, out of hundreds, and then later after its classified pick the model that fits all criterias / scores best?

ZenithAi profil fotoğrafı

ZenithAi1 ay önce

Impressive efficiency gains for long-context encoder workloads

코지베어 🐻 CozyBear profil fotoğrafı

코지베어 🐻 CozyBear1 ay önce

Bidirectional encoders staying fast on CPU at 8k tokens is quietly a bigger deal than most launches this week. Reranking is the part of RAG everyone still ships to a GPU box out of pure habit. Curious how much of that 3.7x survives int8 quantization. Did you benchmark on commodity hardware too?

Mert Kaya profil fotoğrafı

Mert Kaya1 ay önce

Great work!

Sophie profil fotoğrafı

Sophie1 ay önce

350M 跑 128K 上下文延迟多少？

No Body profil fotoğrafı

No Body1 ay önce

Babe wake up liquid AI just dropped a new thalamus routing model

Regi Kusumaatmadja profil fotoğrafı

Regi Kusumaatmadja1 ay önce

interesting!

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New LFM2.5-2.6B hits DeepSeek-V4 level on tool calling and runs 3.7x faster! We ran Liquid AI 's new LFM2.5-2.6B against DeepSeek-V4-Flash on one box with 4x RTX 5090. Both got the same three jobs, and each one only completes if the model fires every tool call Topics: -weather and local time in six cities -one budget into six currencies -four hotels checked and booked for one date Outputs: LFM2.5-2.6B: 35/35 tool-calls, 366 tok/s, 19s DeepSeek-V4-Flash: 35/35 tool-calls, 77 tok/s, 70s Both models made all 35 calls, and both fired twelve of them in one turn. LFM was trained for agents, and tool calling is where that shows. Strong result for a model you can run right on your phone

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As an early look at ongoing work, we deployed LFM2.5-230M on a Unitree G1, running entirely on-device on its onboard NVIDIA Jetson Orin. The model acts as a skill-selection layer, taking in natural-language instructions and decomposing them into sequences of tool calls. After a quick fine-tune, "Hold still for 2s, walk forward at 1 m/s for 3 m, hold a one-leg kneel for 5s, walk back at 0.5 m/s for 3 m" becomes a structured multi-step plan automatically. (3/n)

As an early look at ongoing work, we deployed LFM2.5-230M on a Unitree G1, running entirely on-device on its onboard NVIDIA Jetson Orin. The model acts as a skill-selection layer, taking in natural-language instructions and decomposing them into sequences of tool calls. After a quick fine-tune, "Hold still for 2s, walk forward at 1 m/s for 3 m, hold a one-leg kneel for 5s, walk back at 0.5 m/s for 3 m" becomes a structured multi-step plan automatically. (3/n)

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17,313 görüntüleme • 2 ay önce

QVAC SDK 0.15.0 is live. This release adds multiple prompts batching, brings a native AMD GPU backend to the stack, moves more vision encoders onto mobile GPUs, and adds a second local coding-agent integration. Main highlights: - Prompt batching for the LLM addon. Batch multiple prompts into one job and process them concurrently, with each answer returned the moment its generation finishes. - Native AMD GPU backend. A first-class HIP/ROCm backend in @qvac/vla-ggml, auto-selected over Vulkan with clean fallback when ROCm is absent. - A second local coding agent. OpenClaw joins OpenCode for local, cloud-free agent workflows. AGENTS - OpenCode plugin update (@qvac/opencode-plugin). Aligned with the current SDK, CLI, and AI SDK provider packages. A fresh install runs OpenCode against managed local QVAC models out of the box, from the default qvac/qwen3.5-9b, with no manual qvac serve setup. - OpenClaw plugin (@qvac/openclaw-plugin). A second coding-agent integration alongside OpenCode. A fresh setup installs the plugin, creates a local qvac provider through onboarding, and runs a QVAC model through OpenClaw🦞's local service path. LANGUAGE MODELS - Prompt batching (LLM addon). Batch multiple prompts in one job and run them concurrently, each answer returns the moment its generation finishes, no waiting on the others. - Reasoning-context trimming on hybrid + recurrent models (@qvac/llm-llamacpp). remove_thinking_from_context now works beyond pure-attention models. Same JS API, no throw. VOICE AND SPEECH - Transcription (transcription-parakeet 0.9.0). More robust CPU fallback on GPU failure and a faster Vulkan backend on Pixel 9. - Text-to-speech features (tts-ggml 0.4.0). Adds LavaSR for noise removal and adjustable output frequency up to 48 kHz, plus Japanese via Chatterbox. - Text-to-speech fixes (tts-ggml 0.4.1). CPU fallback on GPU failure, a q8_0 KV crash fix on Metal with Chatterbox. VISION - Qwen3.5 vision encoder on GPU (Android). Image encoder moves onto the phone GPU, with a smarter tile-grid preprocessor and default image-token caps, for flagship Android: Vulkan on Mali (Pixel 9 Pro) and OpenCL on Adreno 830 (Galaxy S25). - Gemma-4 vision encoder on GPU (Android). Vision encoder runs on the phone GPU instead of CPU, same flagship Android targets. PLATFORM AND PERFORMANCE - AMD GPU backend (@qvac/vla-ggml). Native HIP/ROCm backend, auto-selected over Vulkan with clean fallback when ROCm is absent (Linux x64 only). Comes with ~23% faster than Vulkan, ~14% faster than PyTorch-ROCm, parity preserved. Unified code style. A cleaner, more consistent, easier-to-contribute codebase. Let's build. npm install @qvac/sdk

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QVAC

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UC Berkeley just open-sourced FreeToken. (2–4x faster local LLM inference than Ollama) the results are wild: - Qwen3.6-35B on an 8GB GPU at 39.3 tokens/s - DeepSeek-V4-Flash 284B on a 32GB GPU at 22 tokens/s - GLM-5.2 753B on a 96GB GPU at 14.9 tokens/s a 35B model at 16-bit precision needs about 70GB just for its weights. even at 4 bits it is close to 18GB, and FreeToken serves it on an 8GB GPU. let me explain how: all three models mentioned above are Mixture-of-Experts, and that is what FreeToken takes advantage of. each layer holds hundreds of separate experts plus a small router that picks a few of them per token. Qwen3.6-35B activates roughly 3B of its 35B parameters per token. DeepSeek-V4-Flash picks 6 of 256 experts per layer, so 13B of its 284B run at a time. so compute was never the bottleneck. the weights a single step touches fit comfortably on a consumer GPU. every expert the router might pick still has to exist somewhere. they sit in system RAM, and the GPU keeps a cache of the ones the model has been using recently. so everything comes down to what happens when the router picks an expert that is not on the GPU. there are two ways to serve that miss: 1. copy it over PCIe and run it on the GPU 2. run it on the CPU, where it already lives both read from the same system memory, so they compete for one pool of bandwidth instead of adding to each other. existing engines pick one option and freeze it when the model loads. but routing changes on every token, so a fixed choice misses most of what the model asks for. FreeToken measures both bandwidths on your machine and splits each step's misses between the two paths in proportion. the GPU and CPU results then merge exactly, with no approximation. two machines with the same GPU can end up wanting opposite strategies, which I did not expect. a 5090 in a gaming desktop should push nearly everything over PCIe, while an 8GB laptop is better off computing most misses on the CPU. none of that is readable off a spec sheet, so the engine profiles it once per machine. the second half of the design is about agents. coding agents constantly rewrite their own history, and every edit normally forces thousands of tokens back through prefill. FreeToken saves its checkpoints at the exact boundaries agent frameworks cut on, so it only reprocesses the new part. its slowest first token stays under 44 seconds, while llama.cpp peaks at 232 and KTransformers at 946. it serves the OpenAI and Anthropic APIs under Apache 2.0, so Claude Code and Codex can point at it directly. releasing weights publicly decides who can download a model, not who can afford to run one. frontier open models keep shipping, and running them still assumes a rented cluster. meanwhile there are over a hundred million consumer machines with discrete GPUs sitting mostly idle. closing that gap was never a hardware problem, and work like this is what turns open weights into something you can actually use. paper: repo: almost every idea in this post, from why memory bandwidth decides the outcome to why moving weights costs more than computing on them, comes straight out of how a GPU is built. I wrote a detailed primer on that. the article is quoted below.

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Introducing PAN — MBZUAI’s New World Model for Interactive Intelligence Developed by MBZUAI’s Institute of Foundation Models, PAN is built for simulation, prediction, and agentic reasoning. Unlike traditional video generators that only output frames, PAN maintains a persistent internal state that evolves when guided with natural language. Its Generative Latent Prediction architecture combines: • A latent encoder to capture the world state • A dynamics module that evolves that state step-by-step • A video diffusion decoder that visualizes outcomes By decoding at every step using a causal sliding-window diffusion process, PAN stays grounded in real-world physics and maintains long-horizon continuity, a leap beyond single-shot models. Evaluated on action fidelity, long-horizon stability, and simulative planning, PAN delivers state-of-the-art performance compared to open models and rivals leading commercial systems. For robotics, autonomy, and decision support, PAN is a foundation for the next wave of intelligent, foresight-driven AI.

MBZUAI

98,785 görüntüleme • 10 ay önce

$THIS CLUSTER OF 4 MAC STUDIOS USES LESS POWER THAN ONE NVIDIA CHIP - AND COSTS 5X LESS 17:30 the number hits hard - 600 watts for all four Mac Studios - less than one H200 that costs $30,000 on its own instead of one $30,000 GPU you get 4 machines with 1.5TB of memory, 3x more performance and models that simply cannot run on a single chip Apple quietly added RDMA over Thunderbolt in a beta update - latency dropped 99% and the same cluster became 3x faster without buying any new hardware before the update: 5 tokens per second. after: 15.5 - a free upgrade through a software update 8 Nvidia DGX Sparks for the same memory would cost $32,000 - this cluster costs a fraction and gets faster with every update 10x more productive at half the price - and the only thing you need is knowing where to look$

THIS CLUSTER OF 4 MAC STUDIOS USES LESS POWER THAN ONE NVIDIA CHIP - AND COSTS 5X LESS 17:30 the number hits hard - 600 watts for all four Mac Studios - less than one H200 that costs $30,000 on its own instead of one $30,000 GPU you get 4 machines with 1.5TB of memory, 3x more performance and models that simply cannot run on a single chip Apple quietly added RDMA over Thunderbolt in a beta update - latency dropped 99% and the same cluster became 3x faster without buying any new hardware before the update: 5 tokens per second. after: 15.5 - a free upgrade through a software update 8 Nvidia DGX Sparks for the same memory would cost $32,000 - this cluster costs a fraction and gets faster with every update 10x more productive at half the price - and the only thing you need is knowing where to look

Noisy

106,054 görüntüleme • 3 ay önce

THIS 18-YEAR-OLD VIETNAMESE GIRL BUILT A 5x RTX 4090 AI TRAINING RIG - AND MAKES $40,000/MONTH FROM IT 5 RTX 4090s in one PC, Asus ROG, Intel 7980X CPU at $1,590 - and a system that trains AI models while she sleeps CPU barely under load - all the work is done by five 4090s with 120GB of combined VRAM one 4090 on generates $400-800/month - five cards generate $2,000-4,000/month just from renting but she went further - trains custom models on demand and sells access to them - and that's already $40,000/month most people her age are looking for their first job - she built an AI factory in her room 5 GPUs, one room, and a system that earns more than most people make in a year

THIS 18-YEAR-OLD VIETNAMESE GIRL BUILT A 5x RTX 4090 AI TRAINING RIG - AND MAKES $40,000/MONTH FROM IT 5 RTX 4090s in one PC, Asus ROG, Intel 7980X CPU at $1,590 - and a system that trains AI models while she sleeps CPU barely under load - all the work is done by five 4090s with 120GB of combined VRAM one 4090 on generates $400-800/month - five cards generate $2,000-4,000/month just from renting but she went further - trains custom models on demand and sells access to them - and that's already $40,000/month most people her age are looking for their first job - she built an AI factory in her room 5 GPUs, one room, and a system that earns more than most people make in a year

Cortex

96,207 görüntüleme • 3 ay önce

Cognition President Russell Kaplan reveals the Cerebras chips they deployed ran their own models so fast at about 950 tokens per second that the team was confused by the test results, and it enabled them to ship a product experience that wouldn't have been possible otherwise. "One thing that was really fun working on SWE is we were the first kind of Western firm to deploy Cerebras at scale. You know, when we tested their chips, we were really confused because we were like, this is really good. You know, it operates a unique point on the Pareto curve of price and throughput." "And, you know, we were getting about 950 tokens per second on our own models, which was many x faster than what we could get on GPUs for the same size of model. And it was a little bit more expensive for us to serve. They were great partners, and they enabled us to ship a product experience that basically wouldn't have been possible otherwise." "And now Cerebras is more popular. You know, now, you know, OpenAI also uses Cerebras for Spark. I think more people are considering them. But we want to constantly be basically pushing the frontier of, well, what's the next most possible thing that wasn't possible previously? And yeah, doing our own RL and post-training helps with that."

Cognition President Russell Kaplan reveals the Cerebras chips they deployed ran their own models so fast at about 950 tokens per second that the team was confused by the test results, and it enabled them to ship a product experience that wouldn't have been possible otherwise. "One thing that was really fun working on SWE is we were the first kind of Western firm to deploy Cerebras at scale. You know, when we tested their chips, we were really confused because we were like, this is really good. You know, it operates a unique point on the Pareto curve of price and throughput." "And, you know, we were getting about 950 tokens per second on our own models, which was many x faster than what we could get on GPUs for the same size of model. And it was a little bit more expensive for us to serve. They were great partners, and they enabled us to ship a product experience that basically wouldn't have been possible otherwise." "And now Cerebras is more popular. You know, now, you know, OpenAI also uses Cerebras for Spark. I think more people are considering them. But we want to constantly be basically pushing the frontier of, well, what's the next most possible thing that wasn't possible previously? And yeah, doing our own RL and post-training helps with that."

Fireside Alpha

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If you hold $NVDA, here is the long-running agents argument that made Gavin Baker more bullish on compute. Fable 5 (Claude Opus 4.8) and ChatGPT 5.5 both demonstrated long-running agent capability - models that sustain deep reasoning over hours or days, not single queries. Baker's framing on BG2 Pod: imagine if Einstein had thought about fundamental physics 24 hours a day for a year. A model never gets distracted, never sleeps, never ages. The compute implication: more tokens per query, compounding inference revenue. Under 0.2% of people on Earth use AI agentically today. One such user runs 500 CPU cores and 5 GPUs, 24/7. Scale that to any meaningful population and the compute shortage persists indefinitely via Brad Gerstner: Source: BG2 Pod -

If you hold $NVDA, here is the long-running agents argument that made Gavin Baker more bullish on compute. Fable 5 (Claude Opus 4.8) and ChatGPT 5.5 both demonstrated long-running agent capability - models that sustain deep reasoning over hours or days, not single queries. Baker's framing on BG2 Pod: imagine if Einstein had thought about fundamental physics 24 hours a day for a year. A model never gets distracted, never sleeps, never ages. The compute implication: more tokens per query, compounding inference revenue. Under 0.2% of people on Earth use AI agentically today. One such user runs 500 CPU cores and 5 GPUs, 24/7. Scale that to any meaningful population and the compute shortage persists indefinitely via Brad Gerstner: Source: BG2 Pod -

Podcast Alpha

14,087 görüntüleme • 3 ay önce

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Milk Road AI

11,819 görüntüleme • 3 ay önce

THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less

THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less

Noisy

186,592 görüntüleme • 3 ay önce

When people talk about robotics, they usually talk about models, data, or hardware. Few people talk about the infrastructure that lets you iterate on all three quickly. Today we're publishing how we trained Dyna-2 on over 1,000,000 hours of egocentric video, repeatably. At this scale, most of what worked at ten thousand hours did not hold up: • ingestion throughput was capped at 14,000 episode-hours per week — a million hours would have taken over a year • building a training manifest took 48 hours before a run could even start • reading a petabyte from cloud storage during training left GPUs exposed to latency and packet loss 🧵

When people talk about robotics, they usually talk about models, data, or hardware. Few people talk about the infrastructure that lets you iterate on all three quickly. Today we're publishing how we trained Dyna-2 on over 1,000,000 hours of egocentric video, repeatably. At this scale, most of what worked at ten thousand hours did not hold up: • ingestion throughput was capped at 14,000 episode-hours per week — a million hours would have taken over a year • building a training manifest took 48 hours before a run could even start • reading a petabyte from cloud storage during training left GPUs exposed to latency and packet loss 🧵

Dyna Robotics

163,811 görüntüleme • 1 ay önce

Google just proved that bigger isn't always better. Their 308M parameter model is outperforming models 2x its size. Google just released 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝗚𝗲𝗺𝗺𝗮, and it's proving that lightweight embedding models can punch way above their weight class. At just 308M parameters (578MB), it's the new state-of-the-art for models under 500M parameters across MTEB multilingual, English, and code benchmarks. But the really impressive part is that it ranks 8th overall on MTEB(Multilingual, v2) - that's 𝟭𝟳 𝗽𝗹𝗮𝗰𝗲𝘀 above the second-best sub-500M model, and it's delivering performance 𝗰𝗼𝗺𝗽𝗮𝗿𝗮𝗯𝗹𝗲 𝘁𝗼 𝗺𝗼𝗱𝗲𝗹𝘀 𝗻𝗲𝗮𝗿𝗹𝘆 𝗱𝗼𝘂𝗯𝗹𝗲 𝗶𝘁𝘀 𝘀𝗶𝘇𝗲. There are three key parts of their training recipe that sets it apart: 𝟭. 𝗘𝗻𝗰𝗼𝗱𝗲𝗿-𝗗𝗲𝗰𝗼𝗱𝗲𝗿 𝗜𝗻𝗶𝘁𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Instead of starting from a decoder-only Gemma 3 model, they first adapted it to encoder-decoder, then used just the encoder. By basing EmbeddingGemma off an LLM that already has world and language understanding, it gives it a stronger starting point. 𝟮. 𝗧𝗵𝗿𝗲𝗲-𝗟𝗼𝘀𝘀 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 They combine three different loss functions, instead of just having one: • Contrastive loss (NCE) with in-batch negatives and hardness weighting • Spread-out regularization to ensure embeddings utilize the full space (for quantization and ANN retrieval) • Embedding matching distillation from Gemini Embedding - not just learning from relevance scores, but directly aligning the embedding space with the teacher model 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗦𝗼𝘂𝗽𝗶𝗻𝗴 Rather than just averaging checkpoints from the same training run, they use optimization techniques to find multiple specialized training mixtures. Each mixture creates an "expert" model in different domains, and averaging all their parameters creates a final model that's actually better than individual models. Extras: • Matryoshka embeddings supporting 768, 512, 256, and 128 dimensions • Quantization-aware training - maintains quality even at int4 precision • 100+ languages from Gemma 3 pretraining • Exceptional performance on low-resource languages (check their XTREME-UP results) Is it the absolute best embedding model? No - Gemini Embedding still leads overall. But that's not really the point. EmbeddingGemma proves you can achieve state-of-the-art performance in a small package that's actually deployable on-device, in low-latency applications, and in resource-constrained environments. This makes good embeddings accessible for use cases that I'm seeing more and more: offline applications, privacy-sensitive deployments, and high-throughput scenarios where inference cost actually matters. Full paper: Shoutout to the EmbeddingGemma team at Google DeepMind for this awesome open source work 💙 and to Daniel Williams for helping me with this video! 🫶

Google just proved that bigger isn't always better. Their 308M parameter model is outperforming models 2x its size. Google just released 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝗚𝗲𝗺𝗺𝗮, and it's proving that lightweight embedding models can punch way above their weight class. At just 308M parameters (578MB), it's the new state-of-the-art for models under 500M parameters across MTEB multilingual, English, and code benchmarks. But the really impressive part is that it ranks 8th overall on MTEB(Multilingual, v2) - that's 𝟭𝟳 𝗽𝗹𝗮𝗰𝗲𝘀 above the second-best sub-500M model, and it's delivering performance 𝗰𝗼𝗺𝗽𝗮𝗿𝗮𝗯𝗹𝗲 𝘁𝗼 𝗺𝗼𝗱𝗲𝗹𝘀 𝗻𝗲𝗮𝗿𝗹𝘆 𝗱𝗼𝘂𝗯𝗹𝗲 𝗶𝘁𝘀 𝘀𝗶𝘇𝗲. There are three key parts of their training recipe that sets it apart: 𝟭. 𝗘𝗻𝗰𝗼𝗱𝗲𝗿-𝗗𝗲𝗰𝗼𝗱𝗲𝗿 𝗜𝗻𝗶𝘁𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Instead of starting from a decoder-only Gemma 3 model, they first adapted it to encoder-decoder, then used just the encoder. By basing EmbeddingGemma off an LLM that already has world and language understanding, it gives it a stronger starting point. 𝟮. 𝗧𝗵𝗿𝗲𝗲-𝗟𝗼𝘀𝘀 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 They combine three different loss functions, instead of just having one: • Contrastive loss (NCE) with in-batch negatives and hardness weighting • Spread-out regularization to ensure embeddings utilize the full space (for quantization and ANN retrieval) • Embedding matching distillation from Gemini Embedding - not just learning from relevance scores, but directly aligning the embedding space with the teacher model 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗦𝗼𝘂𝗽𝗶𝗻𝗴 Rather than just averaging checkpoints from the same training run, they use optimization techniques to find multiple specialized training mixtures. Each mixture creates an "expert" model in different domains, and averaging all their parameters creates a final model that's actually better than individual models. Extras: • Matryoshka embeddings supporting 768, 512, 256, and 128 dimensions • Quantization-aware training - maintains quality even at int4 precision • 100+ languages from Gemma 3 pretraining • Exceptional performance on low-resource languages (check their XTREME-UP results) Is it the absolute best embedding model? No - Gemini Embedding still leads overall. But that's not really the point. EmbeddingGemma proves you can achieve state-of-the-art performance in a small package that's actually deployable on-device, in low-latency applications, and in resource-constrained environments. This makes good embeddings accessible for use cases that I'm seeing more and more: offline applications, privacy-sensitive deployments, and high-throughput scenarios where inference cost actually matters. Full paper: Shoutout to the EmbeddingGemma team at Google DeepMind for this awesome open source work 💙 and to Daniel Williams for helping me with this video! 🫶

Victoria Slocum

21,610 görüntüleme • 10 ay önce

TurboEdit Instant text-based image editing discuss: We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.

TurboEdit Instant text-based image editing discuss: We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.

AK

16,062 görüntüleme • 2 yıl önce

Tencent's Hy3 lands today with real coding and agent ability and a price that undercuts the models you'd put it against. It's a 295B MoE with only 21B active parameters, a big part of why it stays cheap to serve, plus a 256K context window and an Apache 2.0 license that even outperforms GLM-5.1 although GLM is 2.5x larger. On OpenRouter it runs at $0.14 per million input tokens and $0.58 per million output. For a model Tencent benchmarks against much larger flagships, that pricing is insane. Price-performance-ratio is China's real moat. I already wrote about Tencent Hy3 earlier and the significant leap in quality it represents. Therefore, I'm pleased to recommend this model from Tencent Hy. Open Router link down below: #Hy3 #Hunyuan #AICoding

Tencent's Hy3 lands today with real coding and agent ability and a price that undercuts the models you'd put it against. It's a 295B MoE with only 21B active parameters, a big part of why it stays cheap to serve, plus a 256K context window and an Apache 2.0 license that even outperforms GLM-5.1 although GLM is 2.5x larger. On OpenRouter it runs at $0.14 per million input tokens and $0.58 per million output. For a model Tencent benchmarks against much larger flagships, that pricing is insane. Price-performance-ratio is China's real moat. I already wrote about Tencent Hy3 earlier and the significant leap in quality it represents. Therefore, I'm pleased to recommend this model from Tencent Hy. Open Router link down below: #Hy3 #Hunyuan #AICoding

Chubby♨️

23,439 görüntüleme • 2 ay önce

New short course: Attention in Transformers: Concepts and Code in PyTorch. Last week we released a course on how LLM transformers work. This week, go deeper and learn about the technical ideas behind the attention mechanism, and see how to code it in PyTorch. This course is built with Joshua Starmer, Founder and CEO of StatQuest. The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper: "Attention is All You Need" by Viswani and others, took off because of its highly scalable design. In this course, you’ll learn how the attention mechanism, a key element of transformer-based LLMs, works and implement it in PyTorch. You'll develop deep intuition about building reliable, functional, and scalable AI applications. What you will do: - Understand the evolution of the attention mechanism, a key breakthrough that led to transformers. - Learn the relationships between word embeddings, positional embeddings, and attention. - Learn about the Query, Key, and Value matrices, and how to produce and use them in attention. - Walk through the math required to calculate self-attention and masked self-attention to learn why and how they work. - Understand the difference between self-attention and masked self-attention and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs. - Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention and how they are all incorporated into a transformer. - Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention. There're lots of exciting technical details in this course. Please sign up here:

New short course: Attention in Transformers: Concepts and Code in PyTorch. Last week we released a course on how LLM transformers work. This week, go deeper and learn about the technical ideas behind the attention mechanism, and see how to code it in PyTorch. This course is built with Joshua Starmer, Founder and CEO of StatQuest. The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper: "Attention is All You Need" by Viswani and others, took off because of its highly scalable design. In this course, you’ll learn how the attention mechanism, a key element of transformer-based LLMs, works and implement it in PyTorch. You'll develop deep intuition about building reliable, functional, and scalable AI applications. What you will do: - Understand the evolution of the attention mechanism, a key breakthrough that led to transformers. - Learn the relationships between word embeddings, positional embeddings, and attention. - Learn about the Query, Key, and Value matrices, and how to produce and use them in attention. - Walk through the math required to calculate self-attention and masked self-attention to learn why and how they work. - Understand the difference between self-attention and masked self-attention and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs. - Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention and how they are all incorporated into a transformer. - Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention. There're lots of exciting technical details in this course. Please sign up here:

Andrew Ng

132,516 görüntüleme • 1 yıl önce

🎉 Congrats to MOSI + OpenMOSS on the release of MOSS-Transcribe-Diarize-0.9B — a genuinely impressive end-to-end ASR model for real-world, multi-speaker conversations. We're proud to partner with them for day-0 support on mlx-audio (Python & Swift). Now running local-first on Apple Silicon: 🎙️ transcript + speaker labels + timestamps in one pass 🗣️ unlimited-speaker diarization ⏱️ up to ~90 min of audio per input 🌍 90+ languages 🧠 128K context • Whisper-Medium encoder + Qwen3-0.6B decoder 🎯 hotword boosting for names, products & domain terms Perfect for meetings, podcasts, interviews, and long-form call analysis — no cloud, no data leaving your Mac. Get started now: 🐍 Python > uv pip install -U mlx-audio 🍎 Swift .package(url: " from: "0.1.3") Go build. 🚀

🎉 Congrats to MOSI + OpenMOSS on the release of MOSS-Transcribe-Diarize-0.9B — a genuinely impressive end-to-end ASR model for real-world, multi-speaker conversations. We're proud to partner with them for day-0 support on mlx-audio (Python & Swift). Now running local-first on Apple Silicon: 🎙️ transcript + speaker labels + timestamps in one pass 🗣️ unlimited-speaker diarization ⏱️ up to ~90 min of audio per input 🌍 90+ languages 🧠 128K context • Whisper-Medium encoder + Qwen3-0.6B decoder 🎯 hotword boosting for names, products & domain terms Perfect for meetings, podcasts, interviews, and long-form call analysis — no cloud, no data leaving your Mac. Get started now: 🐍 Python > uv pip install -U mlx-audio 🍎 Swift .package(url: " from: "0.1.3") Go build. 🚀

Prince Canuma

11,639 görüntüleme • 2 ay önce

We release Needle 3: A Sliceable 8-29MB automation foundation model that can match DeepSeek V4 Flash. One set of weights, every depth from 2 to 20 layers a model of its own, 25-121M parameters at CQ2-bit, built on our Simple Attention Networks and running locally at up to 4k tokens/sec decode speed on a Raspberry Pi 5. Needle does not chat. Every turn is a function call: give it the tools your app exposes and it picks the right ones and fills every argument from what the user said, or hand it a schema and it returns a typed record. Ask for something no tool covers and you get an empty list, not a guess. That trade is lets 121M parameters trained on 360B tokens of structured data beat models 10x their size on mobile tool calls and match 2-3x bigger models on structured JSON extraction. It runs on mobiles, wearables, smart home devices, small robots and microcontrollers, with prebuilt engines for macOS, Linux, Windows, Android, iOS, watchOS, tvOS, the browser and WASI hosts. Try it in your browser:

We release Needle 3: A Sliceable 8-29MB automation foundation model that can match DeepSeek V4 Flash. One set of weights, every depth from 2 to 20 layers a model of its own, 25-121M parameters at CQ2-bit, built on our Simple Attention Networks and running locally at up to 4k tokens/sec decode speed on a Raspberry Pi 5. Needle does not chat. Every turn is a function call: give it the tools your app exposes and it picks the right ones and fills every argument from what the user said, or hand it a schema and it returns a typed record. Ask for something no tool covers and you get an empty list, not a guess. That trade is lets 121M parameters trained on 360B tokens of structured data beat models 10x their size on mobile tool calls and match 2-3x bigger models on structured JSON extraction. It runs on mobiles, wearables, smart home devices, small robots and microcontrollers, with prebuilt engines for macOS, Linux, Windows, Android, iOS, watchOS, tvOS, the browser and WASI hosts. Try it in your browser:

Cactus Compute

401,091 görüntüleme • 5 gün önce

🤑Turn Your Idle CPU into Passive Income! ▶️How Much Can You Earn as a Contributor? With VerAI, Contributors share idle CPU/GPU resources to train AI models and earn passive income. Using a simple CPU, you can earn $1.5–$2 per day that’s $45–$60 per month! 💵 The calculation is based on the computational power you provide: a basic CPU (e.g., Intel i5) contributes to lightweight training tasks, earning at this rate. Got a more powerful CPU or GPU? You could earn 5-10x more up to $600/month with a high-end GPU like an RTX 4090! 💵💵💵 ▶️A Game-Changer for Your Income and the Planet!🌎🌱 VerAI isn’t just about earnings it’s about empowerment! Share your resources and watch your monthly income soar, all while helping build the future of AI. Plus, our ecosystem cuts CO2 emissions by 20% per training cycle, making AI training greener than ever. It’s a win-win: you earn, the planet thrives, and Big Tech’s grip loosens! 🌎🌱🌱🌱 ▶️Join the Revolution Now! The future of AI is decentralized, sustainable, and rewarding. With 80K innovators already on our waitlist, VerAI is coming and you don’t want to miss out! 👨‍💻👨‍💻 👉 Don’t miss out join the waitlist today and secure your spot! #VerAI #EarnWithVerAI #SustainableTech #JoinTheFuture #Earncrypto #DecentralizedAI #AIInnovation #VerAI #Waitlist

🤑Turn Your Idle CPU into Passive Income! ▶️How Much Can You Earn as a Contributor? With VerAI, Contributors share idle CPU/GPU resources to train AI models and earn passive income. Using a simple CPU, you can earn $1.5–$2 per day that’s $45–$60 per month! 💵 The calculation is based on the computational power you provide: a basic CPU (e.g., Intel i5) contributes to lightweight training tasks, earning at this rate. Got a more powerful CPU or GPU? You could earn 5-10x more up to $600/month with a high-end GPU like an RTX 4090! 💵💵💵 ▶️A Game-Changer for Your Income and the Planet!🌎🌱 VerAI isn’t just about earnings it’s about empowerment! Share your resources and watch your monthly income soar, all while helping build the future of AI. Plus, our ecosystem cuts CO2 emissions by 20% per training cycle, making AI training greener than ever. It’s a win-win: you earn, the planet thrives, and Big Tech’s grip loosens! 🌎🌱🌱🌱 ▶️Join the Revolution Now! The future of AI is decentralized, sustainable, and rewarding. With 80K innovators already on our waitlist, VerAI is coming and you don’t want to miss out! 👨‍💻👨‍💻 👉 Don’t miss out join the waitlist today and secure your spot! #VerAI #EarnWithVerAI #SustainableTech #JoinTheFuture #Earncrypto #DecentralizedAI #AIInnovation #VerAI #Waitlist

VerAi

13,196 görüntüleme • 1 yıl önce

We are releasing Carbon: a crazy fast DNA model Carbon is 275x faster than the next best model. So fast you can process the whole human genome on a single GPU in <2 days. Here are the tricks we used: When modelling DNA sequences a lot of the performance comes down to tokenizing the sequences in a smart way. BPE tokenizer struggle because there are no whitespaces and character (called base in DNA) level tokenizers waste a lot of compute on too many tokens. Carbon is built with a unique tokenizer: we split sequences in chunks of 6 bases, but during both training and inference we can work with single base resolution. That's similar to having word tokens but resolving them at the character level. All possible thanks to the DNA tokens unique structure. The architecture combined with the tokenizer makes the model 275x faster than the previous SoTA (Evo2) at this size. We built an interactive demo so you can explore how the model can generate DNA sequences, investigate the structure of genes, predict the effect of mutations, generate and fold proteins and even reconstruct parts of the tree of life.

We are releasing Carbon: a crazy fast DNA model Carbon is 275x faster than the next best model. So fast you can process the whole human genome on a single GPU in <2 days. Here are the tricks we used: When modelling DNA sequences a lot of the performance comes down to tokenizing the sequences in a smart way. BPE tokenizer struggle because there are no whitespaces and character (called base in DNA) level tokenizers waste a lot of compute on too many tokens. Carbon is built with a unique tokenizer: we split sequences in chunks of 6 bases, but during both training and inference we can work with single base resolution. That's similar to having word tokens but resolving them at the character level. All possible thanks to the DNA tokens unique structure. The architecture combined with the tokenizer makes the model 275x faster than the previous SoTA (Evo2) at this size. We built an interactive demo so you can explore how the model can generate DNA sequences, investigate the structure of genes, predict the effect of mutations, generate and fold proteins and even reconstruct parts of the tree of life.

Leandro von Werra

405,709 görüntüleme • 4 ay önce