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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 次观看 • 5 个月前 •via X (Twitter)

教育科学技术

Anya Rossi• Live Now

Private livecam show

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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

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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.

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383,130 次观看 • 2 年前

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

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🚀 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

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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.

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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 🧵..

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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

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SkalskiP

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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

337,408 次观看 • 2 年前

We’re also publicly releasing the Seamless Interaction Dataset, with 4,000+ participants and 4,000+ hours of interactions, making it the largest known video dataset of its kind. This dataset enables our audiovisual behavioral models to understand and generate human-like social behaviors, and represents a crucial stepping stone to understanding and modeling how people communicate and behave when they’re together. We’re sharing it here to help the research community advance their work:

We’re also publicly releasing the Seamless Interaction Dataset, with 4,000+ participants and 4,000+ hours of interactions, making it the largest known video dataset of its kind. This dataset enables our audiovisual behavioral models to understand and generate human-like social behaviors, and represents a crucial stepping stone to understanding and modeling how people communicate and behave when they’re together. We’re sharing it here to help the research community advance their work:

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111,054 次观看 • 2 年前

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

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Brendan Hogan

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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

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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

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AK

70,029 次观看 • 1 年前

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Paolo Ardoino 🤖

42,271 次观看 • 2 个月前

This is pretty amazing. Claude with computer use plus a bash tool downloads a random dataset online, installs sklearn, trains a simple classifier on the dataset, and then displays the classifier results in a webpage. All with just one prompt in under 5 minutes.

This is pretty amazing. Claude with computer use plus a bash tool downloads a random dataset online, installs sklearn, trains a simple classifier on the dataset, and then displays the classifier results in a webpage. All with just one prompt in under 5 minutes.

Alex Albert

171,988 次观看 • 1 年前

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New research from Meta FAIR — Meta Explore Theory-of-Mind: Program guided adversarial data generation for theory of mind reasoning. This work includes a new research paper, code & a dataset on Hugging Face. Details + eight more new releases from FAIR ➡️

AI at Meta

38,568 次观看 • 1 年前

YouTube is a LARGE dataset of demonstration videos to train Generalist robot agents, but lacks action data. How can we learn DEXTEROUS skills from them? In #CoRL2024, we explore the problem of learning a Generalist Piano Playing agent from YouTube videos.

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Julen Urain

42,434 次观看 • 1 年前

This is it: A single-person project; Trained from scratch on TPUs (Google TRC) on the one and only SYTNH dataset by pleias; Neuroblast-v3 architecture running on my local vLLM instance Just wow (I'm amazed by how good it looks; speed is incredible, here slightly slowed by high-resolution recording) Todos > needs agentic fine-tuning in the future > needs some fine-grained RL

This is it: A single-person project; Trained from scratch on TPUs (Google TRC) on the one and only SYTNH dataset by pleias; Neuroblast-v3 architecture running on my local vLLM instance Just wow (I'm amazed by how good it looks; speed is incredible, here slightly slowed by high-resolution recording) Todos > needs agentic fine-tuning in the future > needs some fine-grained RL

Mariusz Kurman

32,474 次观看 • 6 个月前

Introducing Meta Locate 3D: a model for accurate object localization in 3D environments. Learn how Meta Locate 3D can help robots accurately understand their surroundings and interact more naturally with humans. You can download the model and dataset, read our research paper, and even try a demo!

Introducing Meta Locate 3D: a model for accurate object localization in 3D environments. Learn how Meta Locate 3D can help robots accurately understand their surroundings and interact more naturally with humans. You can download the model and dataset, read our research paper, and even try a demo!

AI at Meta

81,287 次观看 • 1 年前