Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

HRM-Text 101 is here. This tutorial takes you from zero to one: from setup to fine-tuning to evaluation. Download the base checkpoint. Fine-tune it on a real task. Evaluate the results. End to end, on a single GPU. Watch the tutorial and start building with HRM-Text.

Sapient Intelligence

5,368 subscribers

187,672 Aufrufe • vor 1 Monat •via X (Twitter)

Bildung Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Ex-NVIDIA engineer who built Unsloth explained RL, kernels, reasoning, quantization, and agents in 2 hours 42 minutes - better than $5000 fine-tuning bootcamps. pick the base model -> write triton kernels for 2x faster fine-tune -> quantize to 4-bit -> run GRPO/DPO -> ship a reasoning model on your single GPU. That loop is why Unsloth is the default way to fine-tune Llama, Qwen, Gemma, and Phi on hardware you already own. Unsloth + Triton kernels + 4-bit quantization + GRPO/DPO + single-GPU fine-tuning - that's the stack. Watch and save it, then fine-tune your first model tonight.

Ex-NVIDIA engineer who built Unsloth explained RL, kernels, reasoning, quantization, and agents in 2 hours 42 minutes - better than $5000 fine-tuning bootcamps. pick the base model -> write triton kernels for 2x faster fine-tune -> quantize to 4-bit -> run GRPO/DPO -> ship a reasoning model on your single GPU. That loop is why Unsloth is the default way to fine-tune Llama, Qwen, Gemma, and Phi on hardware you already own. Unsloth + Triton kernels + 4-bit quantization + GRPO/DPO + single-GPU fine-tuning - that's the stack. Watch and save it, then fine-tune your first model tonight.

h100envy

462,401 Aufrufe • vor 3 Tagen

From AI imagination to GPU acceleration - this new tutorial shows how to build a full Astro Zombie Horde from a single image to final render.🧟‍♂️ Using AI tools on the front end and the The Render Network on the back, the entire pipeline comes together in hours - not days. Watch the full breakdown:

From AI imagination to GPU acceleration - this new tutorial shows how to build a full Astro Zombie Horde from a single image to final render.🧟‍♂️ Using AI tools on the front end and the The Render Network on the back, the entire pipeline comes together in hours - not days. Watch the full breakdown:

The Render Network

11,458 Aufrufe • vor 8 Monaten

Here is quick tutorial on the quickest way to repoduce/run NanoChat with minimal GPU setup time using Modal

Here is quick tutorial on the quickest way to repoduce/run NanoChat with minimal GPU setup time using Modal

Adithya S K

17,996 Aufrufe • vor 9 Monaten

New to coding? New to Replit? Here's an end-to-end tutorial on building and deploying an app. ZERO code required.

New to coding? New to Replit? Here's an end-to-end tutorial on building and deploying an app. ZERO code required.

matt palmer

47,045 Aufrufe • vor 1 Jahr

train YOLOv9 on your dataset tutorial - run inference with a pre-trained COCO model - fine-tune model on custom dataset - evaluate the trained model - run inference with a fine-tuned model blogpost: ↓ read more

train YOLOv9 on your dataset tutorial - run inference with a pre-trained COCO model - fine-tune model on custom dataset - evaluate the trained model - run inference with a fine-tuned model blogpost: ↓ read more

SkalskiP

111,874 Aufrufe • vor 2 Jahren

In this benchmark deep-dive, Sapient’s founders William and Guan are joined by research team members Changling and Yasin to unpack HRM-Text’s performance across MATH, DROP, ARC-Challenge, and MMLU. 📊 Beyond the scores, they discuss what each benchmark measures, how HRM-Text compares with larger models, and why efficiency matters. Watch the full discussion to learn more about HRM-Text and Sapient’s leaner path toward general intelligence.

In this benchmark deep-dive, Sapient’s founders William and Guan are joined by research team members Changling and Yasin to unpack HRM-Text’s performance across MATH, DROP, ARC-Challenge, and MMLU. 📊 Beyond the scores, they discuss what each benchmark measures, how HRM-Text compares with larger models, and why efficiency matters. Watch the full discussion to learn more about HRM-Text and Sapient’s leaner path toward general intelligence.

Sapient Intelligence

241,067 Aufrufe • vor 1 Monat

🎉 Meet Krea 2 from Krea, an aesthetic open-source image model ranked #1 text-to-image from an independent lab on Artificial Analysis. Day-0 support is now live in SGLang! Krea 2 ships as two models built to work together: 1️⃣ RAW: undistilled base checkpoint: diverse & malleable, made for fine-tuning, post-training & LoRA 2️⃣ Turbo: 8-step distilled checkpoint: fast, high-quality text-to-image Train LoRAs on RAW, run them on Turbo: base for training, Turbo for fast inference on your own hardware Run it now with SGLang!

🎉 Meet Krea 2 from Krea, an aesthetic open-source image model ranked #1 text-to-image from an independent lab on Artificial Analysis. Day-0 support is now live in SGLang! Krea 2 ships as two models built to work together: 1️⃣ RAW: undistilled base checkpoint: diverse & malleable, made for fine-tuning, post-training & LoRA 2️⃣ Turbo: 8-step distilled checkpoint: fast, high-quality text-to-image Train LoRAs on RAW, run them on Turbo: base for training, Turbo for fast inference on your own hardware Run it now with SGLang!

LMSYS Org

11,099 Aufrufe • vor 19 Tagen

I just released a 2.5 hour long tutorial to show how to build a Mixed Reality game in Unity from start to finish. This is the most in depth tutorial I made on XR with: ✅Passthrough Setup ✅ Scene model ✅ Spawning objects ✅ Navmesh ✅ Publishing your game on the Meta Store!

I just released a 2.5 hour long tutorial to show how to build a Mixed Reality game in Unity from start to finish. This is the most in depth tutorial I made on XR with: ✅Passthrough Setup ✅ Scene model ✅ Spawning objects ✅ Navmesh ✅ Publishing your game on the Meta Store!

Quentin Valembois

12,694 Aufrufe • vor 1 Jahr

This is a distrubing sequence that flew under the radar on Sunday — clock running near end of Q3, less time on game clock than play clock, so taking it to the EOQ is fine. Texans clearly, though, wanna run a play. Fine. What’s NOT fine — (1) the play is VERY late getting called and (2) CJ and the offense seem to have zero clue the quarter is ending. Please tell me I’m off base here, I’d love to be wrong.

This is a distrubing sequence that flew under the radar on Sunday — clock running near end of Q3, less time on game clock than play clock, so taking it to the EOQ is fine. Texans clearly, though, wanna run a play. Fine. What’s NOT fine — (1) the play is VERY late getting called and (2) CJ and the offense seem to have zero clue the quarter is ending. Please tell me I’m off base here, I’d love to be wrong.

Sean Pendergast

1,791,495 Aufrufe • vor 9 Monaten

I recorded a video tutorial on building an AI Code Assistant. Going through the full stack - from data models to the front end. Using Postgres, pg_vector, LangChain, OpenAI, and NextJS.

I recorded a video tutorial on building an AI Code Assistant. Going through the full stack - from data models to the front end. Using Postgres, pg_vector, LangChain, OpenAI, and NextJS.

Gwen (Chen) Shapira

30,463 Aufrufe • vor 1 Jahr

We just reached the text to mobile apps moment. CatDoes v3 lets you go from a single idea to a real iOS, Android and web app by chatting with AI agents. Here is a WhatsApp style app going from blank project to running on an iPhone in one session.

We just reached the text to mobile apps moment. CatDoes v3 lets you go from a single idea to a real iOS, Android and web app by chatting with AI agents. Here is a WhatsApp style app going from blank project to running on an iPhone in one session.

Chubby♨️

24,878 Aufrufe • vor 7 Monaten

LLM post-training used to mean fine-tuning to a downstream task Robotics has been stuck in this setting, needing task-specific fine-tuning for best performance π07 changes this: It works out of the box & outperforms fine-tuned specialists Details:

LLM post-training used to mean fine-tuning to a downstream task Robotics has been stuck in this setting, needing task-specific fine-tuning for best performance π07 changes this: It works out of the box & outperforms fine-tuned specialists Details:

Chelsea Finn

63,780 Aufrufe • vor 2 Monaten

StreamDiT Real-Time Streaming Text-to-Video Generation StreamDiT enables real-time text-to-video generation at 16 FPS on a single GPU (H100)

StreamDiT Real-Time Streaming Text-to-Video Generation StreamDiT enables real-time text-to-video generation at 16 FPS on a single GPU (H100)

AK

43,659 Aufrufe • vor 1 Jahr

Apple built a large foundation model and fine-tuned it on multiple tasks. But they are doing something very clever: They load a single model in memory and use different adapters to specialize the model on the fly. I recorded a video to show you how to write the code to do the same thing Apple is doing. I explain everything step by step. Here is what I'll show you in the video: 1. We'll load two datasets 2. Then load a large model 3. Then, we'll fine-tune the model on both datasets I'll use LoRA to fine-tune the model. This process creates two small adapters, each specializing in solving one of the datasets. The base model's original parameters will remain unchanged. From here: 4. We'll generate a list of tasks 5. We'll load the correct adapter to solve each task The large model I'm using needs 346 MB of memory, but I only need to load it once. Each adapter is only 2.7 MB. I only need to load the base model once and pair it with any of the fine-tuned adapters. Minimum memory footprint and I can solve multiple tasks. Hope this helps!

Apple built a large foundation model and fine-tuned it on multiple tasks. But they are doing something very clever: They load a single model in memory and use different adapters to specialize the model on the fly. I recorded a video to show you how to write the code to do the same thing Apple is doing. I explain everything step by step. Here is what I'll show you in the video: 1. We'll load two datasets 2. Then load a large model 3. Then, we'll fine-tune the model on both datasets I'll use LoRA to fine-tune the model. This process creates two small adapters, each specializing in solving one of the datasets. The base model's original parameters will remain unchanged. From here: 4. We'll generate a list of tasks 5. We'll load the correct adapter to solve each task The large model I'm using needs 346 MB of memory, but I only need to load it once. Each adapter is only 2.7 MB. I only need to load the base model once and pair it with any of the fine-tuned adapters. Minimum memory footprint and I can solve multiple tasks. Hope this helps!

Santiago

84,747 Aufrufe • vor 1 Jahr

Here is a very powerful workflow combining Flux Kontext (from Black Forest Labs) and Kling 2.1 (from Kling AI ). Watch the super easy tutorial at the end and try it out on OpenArt today!

Here is a very powerful workflow combining Flux Kontext (from Black Forest Labs) and Kling 2.1 (from Kling AI ). Watch the super easy tutorial at the end and try it out on OpenArt today!

OpenArt

21,667 Aufrufe • vor 1 Jahr

Introducing the EASIEST way to fine-tune Qwen 2 VL with a Hugging Face dataset! (Link to repo in first reply). You can either fine-tune inside or a Gradio app or with a single line of Python. Simply select your favorite image-text dataset and fine-tune! A special thanks to Faen Zhang and all those at HuggingFace and those behind Qwen 2 VL. blog coming soon!

Introducing the EASIEST way to fine-tune Qwen 2 VL with a Hugging Face dataset! (Link to repo in first reply). You can either fine-tune inside or a Gradio app or with a single line of Python. Simply select your favorite image-text dataset and fine-tune! A special thanks to Faen Zhang and all those at HuggingFace and those behind Qwen 2 VL. blog coming soon!

William J.B. Mattingly

21,466 Aufrufe • vor 1 Jahr

Over 111,111 studio-quality tracks have been created with Eleven Music since we released it 24 hours ago. Learn how to make your own in this tutorial. Start with a single prompt, then edit and fine-tune the structure, style, and lyrics of individual sections in the timeline.

Over 111,111 studio-quality tracks have been created with Eleven Music since we released it 24 hours ago. Learn how to make your own in this tutorial. Start with a single prompt, then edit and fine-tune the structure, style, and lyrics of individual sections in the timeline.

ElevenLabs

52,344 Aufrufe • vor 11 Monaten

Building with LLMs on BNB Chain just got easier. This tutorial walks through MCP setup and shows how to connect LLMs, such as Claude/@openai, to onchain agents and workflows. Watch the tutorial by our DevRel, gwen.eth 👇

Building with LLMs on BNB Chain just got easier. This tutorial walks through MCP setup and shows how to connect LLMs, such as Claude/@openai, to onchain agents and workflows. Watch the tutorial by our DevRel, gwen.eth 👇

BNB Chain Developers

27,076 Aufrufe • vor 3 Monaten

$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 in roughly one day on a $1,000 budget. This opens the door to a new generation of AI that is powerful, accessible, and radically easier to adapt. Theories and research concepts once deemed too expensive to test are officially back in the game. Sapient Intelligence invites you to help us shape a new paradigm for general intelligence.$

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 in roughly one day on a $1,000 budget. This opens the door to a new generation of AI that is powerful, accessible, and radically easier to adapt. Theories and research concepts once deemed too expensive to test are officially back in the game. Sapient Intelligence invites you to help us shape a new paradigm for general intelligence.

Sapient Intelligence

512,207 Aufrufe • vor 1 Monat