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.

Most "agentic" failures happen because the model lacks specific domain knowledge. Here I'm showing how loading a Skills Plugin solves that for dataset generation. I turned a research paper (shows fine-tuning dataset creation from books) into a Skill and just gave a book link and asked it to generate... show more

Muratcan Koylan

21,600 subscribers

104,519 Aufrufe • vor 10 Monaten •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

Introducing Invent a Dataset. Describe the dataset you need. Get a structured, training-ready dataset back. No existing data needed. Dataset creation used to start with collection. Now it starts with specification.

Introducing Invent a Dataset. Describe the dataset you need. Get a structured, training-ready dataset back. No existing data needed. Dataset creation used to start with collection. Now it starts with specification.

adaption

93,053 Aufrufe • vor 2 Monaten

Before Claude Fable 5 got banned, I turned all my fine-tuning research and experiments into a product: (A CLI for data generation to fine-tune AI models). Finetuner uses Codex 5.5 or Opus 4.8 as the orchestrator and Chinese models (DeepSeek v4 Pro, Kimi K2.7, MiMo v2.5, etc.) to generate dataset rows. This system is unique because these are not Python-script-paraphrased datasets. Each and every row is handcrafted by Chinese models based on the orchestrator model's instructions. Now anyone can generate datasets to fine-tune small language models (1B to 30B models). I achieved 10x lower costs and 5x better dataset quality. Releasing the product in a few days. (I'll open-source the skills.)

Before Claude Fable 5 got banned, I turned all my fine-tuning research and experiments into a product: (A CLI for data generation to fine-tune AI models). Finetuner uses Codex 5.5 or Opus 4.8 as the orchestrator and Chinese models (DeepSeek v4 Pro, Kimi K2.7, MiMo v2.5, etc.) to generate dataset rows. This system is unique because these are not Python-script-paraphrased datasets. Each and every row is handcrafted by Chinese models based on the orchestrator model's instructions. Now anyone can generate datasets to fine-tune small language models (1B to 30B models). I achieved 10x lower costs and 5x better dataset quality. Releasing the product in a few days. (I'll open-source the skills.)

CJ Zafir

40,385 Aufrufe • vor 4 Monaten

At Stripe Sessions, we showed how we think agentic commerce will often happen behind the scenes in the course of producing other final products. Here, we show our Claude Code using MPP and Tempo to buy a dataset from Alpha Vantage (YC S18) in the process of generating a research report for me on AI energy usage.

At Stripe Sessions, we showed how we think agentic commerce will often happen behind the scenes in the course of producing other final products. Here, we show our Claude Code using MPP and Tempo to buy a dataset from Alpha Vantage (YC S18) in the process of generating a research report for me on AI energy usage.

John Collison

352,125 Aufrufe • vor 6 Monaten

Introducing `claude-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. Just write a sentence describing the model you want. A chain of AI systems will generate a dataset and train a model for you. And it's open-source.

Introducing `claude-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. Just write a sentence describing the model you want. A chain of AI systems will generate a dataset and train a model for you. And it's open-source.

Matt Shumer

383,376 Aufrufe • vor 2 Jahren

Can 1,000 real-world 3D scenes make a state-of-the-art model better? At OVER Research, we tested it by fine-tuning R3, a state-of-the-art streaming 3D reconstruction model, on roughly 1,000 scenes from the OVER dataset. The result: 25% lower trajectory error on our real-world environments compared to the deployed public checkpoint. And we did it using only 1,000 scenes, the same open dataset anyone can download for free, out of the 285,000+ real-world 3D scenes OVER has today. Read the full OVER Research publication: Download the 1,000-scene dataset on Hugging Face:

Can 1,000 real-world 3D scenes make a state-of-the-art model better? At OVER Research, we tested it by fine-tuning R3, a state-of-the-art streaming 3D reconstruction model, on roughly 1,000 scenes from the OVER dataset. The result: 25% lower trajectory error on our real-world environments compared to the deployed public checkpoint. And we did it using only 1,000 scenes, the same open dataset anyone can download for free, out of the 285,000+ real-world 3D scenes OVER has today. Read the full OVER Research publication: Download the 1,000-scene dataset on Hugging Face:

Over the Reality 🌐

34,785 Aufrufe • vor 2 Monaten

The internet is the greatest dataset ever created. Today, we're launching the Riveter Dataset Builder to make it possible for anyone to get custom, fresh data from a prompt.

The internet is the greatest dataset ever created. Today, we're launching the Riveter Dataset Builder to make it possible for anyone to get custom, fresh data from a prompt.

Abby Grills

24,874 Aufrufe • vor 6 Monaten

🚀 Unitree open-sources UnifoLM-WBT-Dataset — a high-quality real-world humanoid robot whole-body teleoperation (WBT) dataset for open environments. 🥳Publicly available since March 5, 2026, the dataset will continue to receive high-frequency rolling updates. It aims to establish the most comprehensive real-world humanoid robot dataset in terms of scenario coverage, task complexity, and manipulation diversity. 👉 Explore the dataset here:

🚀 Unitree open-sources UnifoLM-WBT-Dataset — a high-quality real-world humanoid robot whole-body teleoperation (WBT) dataset for open environments. 🥳Publicly available since March 5, 2026, the dataset will continue to receive high-frequency rolling updates. It aims to establish the most comprehensive real-world humanoid robot dataset in terms of scenario coverage, task complexity, and manipulation diversity. 👉 Explore the dataset here:

Unitree

5,633,606 Aufrufe • vor 7 Monaten

I'm excited to announce the release of the Naijaweb 🇳🇬 dataset. Naijaweb is a 270,000 (230Million GPT2 tokens) document dataset of webpages which Nigerians have shown interest in, it was cleaned with the same techniques done on the Fineweb dataset by Hugging Face. A thread 🧵..

I'm excited to announce the release of the Naijaweb 🇳🇬 dataset. Naijaweb is a 270,000 (230Million GPT2 tokens) document dataset of webpages which Nigerians have shown interest in, it was cleaned with the same techniques done on the Fineweb dataset by Hugging Face. A thread 🧵..

Saheedniyi

188,409 Aufrufe • vor 2 Jahren

Most robot datasets store one sentence per episode: the task. Real problems need more: a plan, a memory, room to answer questions and change course. We just shipped full language support in the LeRobot dataset format. Now lerobot-annotate points a vision-language model at each episode and writes the subtasks, plans, memory, and grounded Q&A itself - roughly $1.20 for 100 episodes. From there, recipes turn the annotated rows into policy-agnostic training messages, so the same dataset can teach planning, memory, tool calls, or low-level control, no dataset redesign required. Read how it all works on the blog:

Most robot datasets store one sentence per episode: the task. Real problems need more: a plan, a memory, room to answer questions and change course. We just shipped full language support in the LeRobot dataset format. Now lerobot-annotate points a vision-language model at each episode and writes the subtasks, plans, memory, and grounded Q&A itself - roughly $1.20 for 100 episodes. From there, recipes turn the annotated rows into policy-agnostic training messages, so the same dataset can teach planning, memory, tool calls, or low-level control, no dataset redesign required. Read how it all works on the blog:

LeRobot

13,451 Aufrufe • vor 3 Monaten

Introducing Axis Dataset V1 - the simulation dataset for scalable robot manipulation. Can noisy, crowdsourced simulation data support embodied pretraining? Yes—with enough coverage and diversity. Continual pretraining on AXIS dataset V1 lifts π0.5 from 83.9% to 88.8% on LIBERO-Plus, with performance improving consistently as the pretraining data scales from 25% to 100% of the full dataset, showing no clear saturation. Built with researchers from Georgia Tech UC Berkeley Texas A&M University Johns Hopkins University Penn University of Michigan NUS NTU Singapore. ➡️Related links: Paper: Dataset: Project Page: GitHub Codebase:

Introducing Axis Dataset V1 - the simulation dataset for scalable robot manipulation. Can noisy, crowdsourced simulation data support embodied pretraining? Yes—with enough coverage and diversity. Continual pretraining on AXIS dataset V1 lifts π0.5 from 83.9% to 88.8% on LIBERO-Plus, with performance improving consistently as the pretraining data scales from 25% to 100% of the full dataset, showing no clear saturation. Built with researchers from Georgia Tech UC Berkeley Texas A&M University Johns Hopkins University Penn University of Michigan NUS NTU Singapore. ➡️Related links: Paper: Dataset: Project Page: GitHub Codebase:

Axis Robotics

114,840 Aufrufe • vor 3 Monaten

Introducing `gpt-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. **Just write a sentence describing the model you want.** A chain of AI systems will generate a dataset and train a model for you. And it's open-source:

Introducing `gpt-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. Just write a sentence describing the model you want. A chain of AI systems will generate a dataset and train a model for you. And it's open-source:

Matt Shumer

337,464 Aufrufe • vor 3 Jahren

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

111,876 Aufrufe • vor 2 Jahren

Very excited to release the Open X-Embodiment Dataset today — the largest robot dataset to date with 1M+ trajectories! Robotics needs more data & this is a big step! There’s lots to unpack here, so let’s do a deep dive into the dataset! 🧵1/15

Very excited to release the Open X-Embodiment Dataset today — the largest robot dataset to date with 1M+ trajectories! Robotics needs more data & this is a big step! There’s lots to unpack here, so let’s do a deep dive into the dataset! 🧵1/15

Karl Pertsch

111,195 Aufrufe • vor 3 Jahren

Continued working on the ego-dex dataset, I ported the entire test set to Rerun and created a Gradio app to view it! Links below VVV This allows for a straightforward way to explore each episode of the (test) dataset and better understand how the hand-tracking and slam systems performed. I had to sadly reencode the videos to AV1, which took up a ton of time (nearly 2 hours of wall time for just the test dataset) Next up is taking this representative dataset and making it amenable to training. I'll start with something easy, such as pose estimation, as it's what I'm most familiar with, but the goal is to allow RRD Webdataset standard.

Continued working on the ego-dex dataset, I ported the entire test set to Rerun and created a Gradio app to view it! Links below VVV This allows for a straightforward way to explore each episode of the (test) dataset and better understand how the hand-tracking and slam systems performed. I had to sadly reencode the videos to AV1, which took up a ton of time (nearly 2 hours of wall time for just the test dataset) Next up is taking this representative dataset and making it amenable to training. I'll start with something easy, such as pose estimation, as it's what I'm most familiar with, but the goal is to allow RRD Webdataset standard.

Pablo Vela

37,193 Aufrufe • vor 1 Jahr

Very nice Colaboratory notebook from adaption staff showing how use invent a dataset in a few lines of code. 🔥

Very nice Colaboratory notebook from adaption staff showing how use invent a dataset in a few lines of code. 🔥

Sara Hooker

13,767 Aufrufe • vor 1 Monat

introducing AlphaLab -an autonomous system that runs quantitative research end-to-end give it a dataset + goal, it explores, reads papers, builds evals, and runs parallel experiments across a GPU cluster, learning&evolving from the results all details, code, and paper below

introducing AlphaLab -an autonomous system that runs quantitative research end-to-end give it a dataset + goal, it explores, reads papers, builds evals, and runs parallel experiments across a GPU cluster, learning&evolving from the results all details, code, and paper below

Brendan Hogan

180,535 Aufrufe • vor 7 Monaten

Meta FAIR recently released the Seamless Interaction Dataset, the largest known high-quality video dataset of its kind, with: 4,000+ diverse participants 4,000+ hours of footage 65k+ interactions 5,000+ annotated samples This dataset of full-body, in-person, face-to-face interaction videos represents a crucial stepping stone to understanding and modeling how people communicate and behave when they’re together—advancing AI's ability to generate more natural conversations and human-like gestures. Download the dataset on Hugging Face: Learn more about the dataset:

Meta FAIR recently released the Seamless Interaction Dataset, the largest known high-quality video dataset of its kind, with: 4,000+ diverse participants 4,000+ hours of footage 65k+ interactions 5,000+ annotated samples This dataset of full-body, in-person, face-to-face interaction videos represents a crucial stepping stone to understanding and modeling how people communicate and behave when they’re together—advancing AI's ability to generate more natural conversations and human-like gestures. Download the dataset on Hugging Face: Learn more about the dataset:

AI at Meta

24,133 Aufrufe • vor 1 Jahr

GameFactory Creating New Games with Generative Interactive Videos present GameFactory, a generalizable world model that learns from a small-scale dataset of Minecraft game videos. By leveraging the prior knowledge of a pretrained video diffusion model, it can create new games in an open domain.

GameFactory Creating New Games with Generative Interactive Videos present GameFactory, a generalizable world model that learns from a small-scale dataset of Minecraft game videos. By leveraging the prior knowledge of a pretrained video diffusion model, it can create new games in an open domain.

AK

70,922 Aufrufe • vor 1 Jahr