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Introducing HRM-Text. An ultra-lean 1B-parameter reasoning language model designed to deliver strong general performance with a fraction of the data, compute, and infrastructure. Trained on just 40B structured tokens, HRM-Text achieves competitive performance while using ~1/1000 of the training data of comparable models. The kicker? The full model trains... show more

Sapient Intelligence

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519,148 Aufrufe • vor 5 Monaten •via X (Twitter)

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34 Kommentare

Profilbild von Sapient Intelligence

Sapient Intelligencevor 5 Monaten

Download HRM-Text 🔗 Github: Hugging Face:

Profilbild von Flavius Burca

Flavius Burcavor 5 Monaten

Am I missing something ? HRM-Text is trained on a corpus where every benchmark's training set is present and often upsampled 10×. Of course you hit the same benchmark score with 150× less data when 150× more of your data is on-distribution for the eval.

Profilbild von AI_Explorer

AI_Explorervor 5 Monaten

Watched it twice. The $1k pretraining number is going to do more for independent research than any open-weights release this year

Profilbild von William Wale

William Walevor 5 Monaten

Those are impressive results. Any reason you didn’t scale it up? It’s a bit strange to have a flagship that trains for a thousand dollars.

Profilbild von Flavius Burca

Flavius Burcavor 5 Monaten

The training mix (data_io) includes GSM8k-train ×10, MATH-train ×10, and FLAN (which contains HellaSwag/ARC/BoolQ/Winogrande train splits). Most of the 150× saving is the data mix, not the architecture. But I do have to applaude the ingenious H/L split :)

Profilbild von ρ:ɡeon

ρ:ɡeonvor 5 Monaten

those are the numbers for Qwen3 Qwen3.5s dataset size has not been disclosed and if you really believe in this AND its so cheap you should scale it up to at least 32B range and get llama.cpp compatability otherwise nobody can even use it or care about it

Profilbild von Crof AI

Crof AIvor 5 Monaten

Very interesting! What about for bigger models larger than 1b?

Profilbild von DevHunterAI

DevHunterAIvor 5 Monaten

$1000 for a 1B parameter is excessive. I full pretrained my 1.5B RLM model with an RTX Pro 6000 for 13 hours. It cost less than $4(with Google Colab).

Profilbild von Knowix

Knowixvor 5 Monaten

looks promising, can't wait to try it out. I really need a small parameter model that I can run on my old PC

Profilbild von Brunão

Brunãovor 5 Monaten

training a 1B model in a day for $1k is insane the barrier to AI research just dropped through the floor

Profilbild von Adam Ritter

Adam Rittervor 5 Monaten

Amazing result, this is where I don't understand why you didn't start a company instead of letting the LLM companies just use it to train much smarter models.

Profilbild von Everlier

Everliervor 5 Monaten

This release is incompatible with me going to sleep in time

Profilbild von Jules 𝕏

Jules 𝕏vor 5 Monaten

Brilliant. Hierarchical dual-timescale reasoning in a 1B model that beats far larger ones on 40B tokens is exactly the efficiency breakthrough we need. This is how we get to real intelligence, not just scale.

Profilbild von himanshu

himanshuvor 5 Monaten

awesome work, congrats!

Profilbild von Ziwen

Ziwenvor 5 Monaten

wait it's only 1B and trained in a day?? That's wild

Profilbild von 𝐲𝐮𝐫𝐨

𝐲𝐮𝐫𝐨vor 5 Monaten

"chain-of-thought" slander for 5 whole mins. great approach, moving from grid-based puzzling in HRM to implementing latent recursive reasoning in language modelling through HRM-text.

Profilbild von Sapient Intelligence

Sapient Intelligencevor 5 Monaten

Paper:

Profilbild von BruzWJ

BruzWJvor 5 Monaten

one day and a thousand bucks is wild, but comparable models is doing a lot of work in that claim what evals are you running and what's the accuracy gap?

Profilbild von the.PM

the.PMvor 5 Monaten

That's super, so maybe we can use our new approach of cloning the LLM behavioural shadow here for only 10 USD and then inference it with CPU. Have to investigate.

Profilbild von Yuli Kay

Yuli Kayvor 5 Monaten

I’ve been using my personal agent for a while now So this could be a game-changer!

Profilbild von Ryan Dale

Ryan Dalevor 5 Monaten

This is amazing

Profilbild von TechGeekDavid

TechGeekDavidvor 5 Monaten

40B structured tokens, 1B params. Tokenization doing the heavy lifting. What counts as 'structured'?

Profilbild von Adhil Parammel

Adhil Parammelvor 5 Monaten

Tried in Colab T4, it's not even forming a coherent sentence 😭

Profilbild von Oogie

Oogievor 5 Monaten

$1k pretraining cost is the part that broke my brain

Profilbild von Harris

Harrisvor 5 Monaten

Where is the paper?

Profilbild von Latent Local

Latent Localvor 5 Monaten

Def interesting stuff. Would love to see if this scales. What kind of context length can you use?

Profilbild von Chemist 🧪

Chemist 🧪vor 5 Monaten

1k to train a 1b model, big labs about to cry into their gpu invoices

Profilbild von Yigido

Yigidovor 5 Monaten

looks awesomeee i watchedd

Profilbild von tara_

tara_vor 5 Monaten

wait… a 1B reasoning model trained for around $1k? that’s actually insane

Profilbild von Yuki Eliot

Yuki Eliotvor 5 Monaten

Finally something that doesn't need a million GPUs lol.

Profilbild von Synthia

Synthiavor 5 Monaten

The most interesting part is the compute barrier collapsing this fast .. feels like we’re entering the era of garage-built AI again

Profilbild von J A Z I I

J A Z I Ivor 5 Monaten

The real AGI is already here

Profilbild von veno‎ 𓅃

veno‎ 𓅃vor 5 Monaten

the $1k pretrain cost is what hit me. small teams can actually do this now

Profilbild von Oxdave

Oxdavevor 5 Monaten

The future of AGI

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Y Combinator

128,225 Aufrufe • vor 6 Monaten

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

Tencent Hy

89,430 Aufrufe • vor 1 Jahr

Last week we released Meta Chameleon: a new mixed-modal research model from Meta FAIR. Get the models ➡️ The 7B & 34B safety tuned models we’ve released can take any combination of text and images as input and produce text outputs using a new early fusion approach. While some LLMs have separate image and text encoders or decoders, Chameleon is one of the first publicly released approaches using a single unified architecture. We’re releasing Chameleon models under a research license to help democratize access to foundational mixed-modal models & further research on early fusion. Approach & training details in the paper ➡️

Last week we released Meta Chameleon: a new mixed-modal research model from Meta FAIR. Get the models ➡️ The 7B & 34B safety tuned models we’ve released can take any combination of text and images as input and produce text outputs using a new early fusion approach. While some LLMs have separate image and text encoders or decoders, Chameleon is one of the first publicly released approaches using a single unified architecture. We’re releasing Chameleon models under a research license to help democratize access to foundational mixed-modal models & further research on early fusion. Approach & training details in the paper ➡️

AI at Meta

54,428 Aufrufe • vor 2 Jahren

Autonomous driving with Chain of Thought - autopilot thinking out loud in text! LINGO-1 is the most interesting work I've read in autodriving for a while. Before: perception -> driving action After: perception -> textual reasoning -> action LINGO-1 trains a video-language model that comments on the ongoing scene. You can ask it to explain its decisions ("why are you stopped?") and planning ("what are you gonna do next?"). The explicit reasoning step comes with key benefits: - Explainability: driving models are no longer a mysterious blackbox that you pray for safety. - Counterfactuals: it's able to imagine scenarios that are not in the training data, and reason through how to handle them correctly. - Long-tail programming: there are soooo many edge cases in driving. It's impossible to have good data coverage on everything. Instead of collecting 1000s of examples to "neural program" a case, you can now have a human teacher write prompts to explain a handful of examples. LINGO-1 is closely related to a few works in game AI: - MineDojo (my team's work at NVIDIA, learns a reward model that aligns Minecraft gameplay videos with their transcripts. The model, called "MineCLIP", is able to ground commentary text in the video pixels. - Thought Cloning (Jeff Clune): pixel -> language -> action loop in gridworlds.

Autonomous driving with Chain of Thought - autopilot thinking out loud in text! LINGO-1 is the most interesting work I've read in autodriving for a while. Before: perception -> driving action After: perception -> textual reasoning -> action LINGO-1 trains a video-language model that comments on the ongoing scene. You can ask it to explain its decisions ("why are you stopped?") and planning ("what are you gonna do next?"). The explicit reasoning step comes with key benefits: - Explainability: driving models are no longer a mysterious blackbox that you pray for safety. - Counterfactuals: it's able to imagine scenarios that are not in the training data, and reason through how to handle them correctly. - Long-tail programming: there are soooo many edge cases in driving. It's impossible to have good data coverage on everything. Instead of collecting 1000s of examples to "neural program" a case, you can now have a human teacher write prompts to explain a handful of examples. LINGO-1 is closely related to a few works in game AI: - MineDojo (my team's work at NVIDIA, learns a reward model that aligns Minecraft gameplay videos with their transcripts. The model, called "MineCLIP", is able to ground commentary text in the video pixels. - Thought Cloning (Jeff Clune): pixel -> language -> action loop in gridworlds.

Jim Fan

552,849 Aufrufe • vor 3 Jahren

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

AK

83,681 Aufrufe • vor 3 Jahren