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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 просмотров • 5 месяцев назад •via X (Twitter)

Образование Наука и технологии

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

37,175 просмотров • 4 дней назад

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

383,130 просмотров • 2 лет назад

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

345,072 просмотров • 1 месяц назад

🚀 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.

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Abby Grills

24,611 просмотров • 2 месяцев назад

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

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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:

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Matt Shumer

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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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AI at Meta

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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

111,054 просмотров • 2 лет назад

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

177,073 просмотров • 2 месяцев назад

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 просмотров • 1 год назад

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

23,836 просмотров • 11 месяцев назад

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

70,029 просмотров • 1 год назад

$QVAC SDK will support in 0.9.0 (gonna be release in ~10 days) LoRA fine-tuning directly on-device, letting developers customize LLMs with their own data without sending anything to the cloud. You just load a base model, point it at your training dataset, and get a lightweight LoRA adapter back — all running locally. The fine-tuned model can then be used for inference immediately, with no extra setup. Why it matters: LoRA (Low-Rank Adaptation) fine-tuning lets you specialize a general-purpose language model for your specific use case — like matching a brand's tone, mastering domain terminology, or following a particular output format — using a fraction of the compute a full fine-tune would require. QVAC handles the entire workflow locally: dataset preparation, training with configurable hyperparameters, checkpoint saving, and seamless inference with the resulting adapter. Your data never leaves the device. The developer experience: Fine-tuning with QVAC is as simple as calling "sdk.finetune()" with your dataset and a few hyperparameters. Training runs entirely on your local hardware, produces a compact LoRA adapter file, and supports pause/resume so you can stop a job and pick it back up without losing progress. The result plugs straight into QVAC's inference pipeline — no model conversion, no deployment step, just immediate local completions with your fine-tuned model.$

QVAC SDK will support in 0.9.0 (gonna be release in ~10 days) LoRA fine-tuning directly on-device, letting developers customize LLMs with their own data without sending anything to the cloud. You just load a base model, point it at your training dataset, and get a lightweight LoRA adapter back — all running locally. The fine-tuned model can then be used for inference immediately, with no extra setup. Why it matters: LoRA (Low-Rank Adaptation) fine-tuning lets you specialize a general-purpose language model for your specific use case — like matching a brand's tone, mastering domain terminology, or following a particular output format — using a fraction of the compute a full fine-tune would require. QVAC handles the entire workflow locally: dataset preparation, training with configurable hyperparameters, checkpoint saving, and seamless inference with the resulting adapter. Your data never leaves the device. The developer experience: Fine-tuning with QVAC is as simple as calling "sdk.finetune()" with your dataset and a few hyperparameters. Training runs entirely on your local hardware, produces a compact LoRA adapter file, and supports pause/resume so you can stop a job and pick it back up without losing progress. The result plugs straight into QVAC's inference pipeline — no model conversion, no deployment step, just immediate local completions with your fine-tuned model.

Paolo Ardoino 🤖

42,271 просмотров • 2 месяцев назад

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 просмотров • 1 год назад

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 ➡️

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 просмотров • 1 год назад

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.

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.

Julen Urain

42,434 просмотров • 1 год назад

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 просмотров • 6 месяцев назад

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 просмотров • 1 год назад