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Introducing `AutoRL` 📈 The world's simplest way to train a task-specific LLM with RL. *Just write a SENTENCE describing the model you want.* A chain of AI systems will generate data + rubrics and train a model for you. Powered by ART, it's open source. Link in thread:

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Matt Shumer

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150,107 görüntüleme • 1 yıl önce •via X (Twitter)

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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:
1:01

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 görüntüleme • 3 yıl önce

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.
0:42

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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383,208 görüntüleme • 2 yıl önce

Introducing `gpt-author v2` 📕✍️ One prompt -> an entire fantasy novel! Just describe the high-level details, and a chain of AI systems will write an entire book for you in minutes. - complete w/ cover art - export to ebook format And it's open-source:
0:52

Introducing `gpt-author v2` 📕✍️ One prompt -> an entire fantasy novel! Just describe the high-level details, and a chain of AI systems will write an entire book for you in minutes. - complete w/ cover art - export to ebook format And it's open-source:

Matt Shumer

276,580 görüntüleme • 2 yıl önce

Introducing `gpt-author` 📕✍️ One prompt -> an entire fantasy novel! Just describe the high-level details, and a chain of AI systems will write an entire book for you in minutes. - complete w/ cover art - export to Kindle store And it's open-source:
0:24

Introducing `gpt-author` 📕✍️ One prompt -> an entire fantasy novel! Just describe the high-level details, and a chain of AI systems will write an entire book for you in minutes. - complete w/ cover art - export to Kindle store And it's open-source:

Matt Shumer

2,101,780 görüntüleme • 3 yıl önce

Introducing `Claude-Author` 📕✍️ One prompt -> an entire novel! Just describe the high-level details, and a chain of AI systems will write an entire book for you in minutes. - complete w/ cover art - packages your book as a real e-book And it's open-source!
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Introducing `Claude-Author` 📕✍️ One prompt -> an entire novel! Just describe the high-level details, and a chain of AI systems will write an entire book for you in minutes. - complete w/ cover art - packages your book as a real e-book And it's open-source!

Matt Shumer

671,966 görüntüleme • 2 yıl önce

Introducing `claude-researcher` 📈 A powerful Claude 3 research agent that delivers thorough reports in record time. Just provide an topic, and a chain of AIs with **access to Google** will generate an incredibly comprehensive report for you. And it's open-source!
0:37

Introducing `claude-researcher` 📈 A powerful Claude 3 research agent that delivers thorough reports in record time. Just provide an topic, and a chain of AIs with **access to Google** will generate an incredibly comprehensive report for you. And it's open-source!

Matt Shumer

397,549 görüntüleme • 2 yıl önce

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.
2:52

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 görüntüleme • 2 yıl önce

this is why open-source wins every time.. open weights allow you to invent new model capabilities, train + generate locally (crucial for NDA'd and personal data), push the limits of what's currently possible + lean into a huge community of experts for advice and inspiration 🖤
2:35

this is why open-source wins every time.. open weights allow you to invent new model capabilities, train + generate locally (crucial for NDA'd and personal data), push the limits of what's currently possible + lean into a huge community of experts for advice and inspiration 🖤

Ingi Erlingsson 🪄

16,877 görüntüleme • 3 ay önce

Introducing `claude-prompt-engineer` ✍️ An agent that creates optimal Claude 3 prompts. Just describe a task, and a chain of AIs will: - Generate many possible prompts - Test them in a ranked tournament - Return the best one And it's open-source:
0:41

Introducing `claude-prompt-engineer` ✍️ An agent that creates optimal Claude 3 prompts. Just describe a task, and a chain of AIs will: - Generate many possible prompts - Test them in a ranked tournament - Return the best one And it's open-source:

Matt Shumer

767,706 görüntüleme • 2 yıl önce

📢 New short course in collaboration with Google: Build and Train an LLM with JAX. In this course, you’ll implement and train a 20M-parameter MiniGPT-style language model from scratch using JAX, the open-source library behind Gemini. You’ll build the model architecture, load and preprocess training data, implement the training loop, save checkpoints, and generate text through a chat interface. Taught by Chris Achard, Developer Relations Engineer on Google’s TPU Software team. Enroll now:
1:54

📢 New short course in collaboration with Google: Build and Train an LLM with JAX. In this course, you’ll implement and train a 20M-parameter MiniGPT-style language model from scratch using JAX, the open-source library behind Gemini. You’ll build the model architecture, load and preprocess training data, implement the training loop, save checkpoints, and generate text through a chat interface. Taught by Chris Achard, Developer Relations Engineer on Google’s TPU Software team. Enroll now:

DeepLearning.AI

46,065 görüntüleme • 5 ay önce

NEWS: Nvidia has just introduced "Cosmos," a world foundation model created to understand the physical world. The model can generate synthetic data to train robotics.
2:34

NEWS: Nvidia has just introduced "Cosmos," a world foundation model created to understand the physical world. The model can generate synthetic data to train robotics.

Sawyer Merritt

254,296 görüntüleme • 1 yıl önce

We just released the world's first open-source humanoid physical strike AI! The AI is trained with large-scale self-play RL with a pre-trained ASE model from Jason Peng. Thank ZiyiBird for this amazing work.
1:11

We just released the world's first open-source humanoid physical strike AI! The AI is trained with large-scale self-play RL with a pre-trained ASE model from Jason Peng. Thank ZiyiBird for this amazing work.

Flood Sung

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Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.
0:26

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 görüntüleme • 12 gün önce

A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to generate synthetic video data with verbs and scenarios not used in the world model’s post-training. ⦿ Auto-label synthetic video data with action sequences. ⦿ Train robot policies using only synthetic data. That's it. Deploy zero-shot to a real humanoid robot.
2:05

A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to generate synthetic video data with verbs and scenarios not used in the world model’s post-training. ⦿ Auto-label synthetic video data with action sequences. ⦿ Train robot policies using only synthetic data. That's it. Deploy zero-shot to a real humanoid robot.

The Humanoid Hub

20,968 görüntüleme • 1 yıl önce

Fiverr just launched a platform for freelancers to train a custom AI model, and "sell" the ability to generate work with it. They're pitching it as a way for creators to scale the number of projects they can handle at once. Models are trainable on text, image, and sound.
0:24

Fiverr just launched a platform for freelancers to train a custom AI model, and "sell" the ability to generate work with it. They're pitching it as a way for creators to scale the number of projects they can handle at once. Models are trainable on text, image, and sound.

Olivia Moore

117,880 görüntüleme • 1 yıl önce

To generate 3D models in Codex with text-to-cad: Start a new project, write your prompt for a 3D model, and include a link to clone the text-to-cad repo. Codex handles the rest. The repo is open source and all scripts / ui run locally. Link below.
0:21

To generate 3D models in Codex with text-to-cad: Start a new project, write your prompt for a 3D model, and include a link to clone the text-to-cad repo. Codex handles the rest. The repo is open source and all scripts / ui run locally. Link below.

Jake Fitzgerald

22,113 görüntüleme • 3 ay önce

David Sacks: Anthropic Is Trying to Crush Open Source AI and the American Developers Who Use It David Sacks: “I know people don't have a lot of sympathy for Chinese companies, that's fine. I'm not defending Chinese companies. I'm defending American developers who need to be able to use everything in the public domain. And let me give you an example. Cursor rolled out its new product, Composer 2. They were able to post-train that model using Kimi K2.5 on their own proprietary coding data. They started with a Chinese open source model, and then they used their own data, and they came up with a new derivative product. This is the way that open source works. You take things that are in the public domain, you fork them, you make them your own. And by the way, once it's in the public domain, it's not a Chinese model anymore. No data is going back to China, nothing's going back to China. An American company has taken open source contributions in the public domain, made it their own, and then developed their own model. And if you say that American companies can't do that, or that somehow it's tainted with IP theft, you are basically going to put a dagger through the heart of the entire American open source ecosystem. And that is exactly what Anthropic wants, because they do not want to have the competition.”
0:59

David Sacks: Anthropic Is Trying to Crush Open Source AI and the American Developers Who Use It David Sacks: “I know people don't have a lot of sympathy for Chinese companies, that's fine. I'm not defending Chinese companies. I'm defending American developers who need to be able to use everything in the public domain. And let me give you an example. Cursor rolled out its new product, Composer 2. They were able to post-train that model using Kimi K2.5 on their own proprietary coding data. They started with a Chinese open source model, and then they used their own data, and they came up with a new derivative product. This is the way that open source works. You take things that are in the public domain, you fork them, you make them your own. And by the way, once it's in the public domain, it's not a Chinese model anymore. No data is going back to China, nothing's going back to China. An American company has taken open source contributions in the public domain, made it their own, and then developed their own model. And if you say that American companies can't do that, or that somehow it's tainted with IP theft, you are basically going to put a dagger through the heart of the entire American open source ecosystem. And that is exactly what Anthropic wants, because they do not want to have the competition.”

The All-In Podcast

277,664 görüntüleme • 22 gün önce

this guy got tired of going back and forth with Claude Code describing what's wrong with the UI so he built a tool that lets you DRAW DIRECTLY on the screen claude renders something. if it's wrong you just circle the problem, add an arrow, write a note ai sees your annotations and fixes it way faster than describing it by text AND it's free + open source
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this guy got tired of going back and forth with Claude Code describing what's wrong with the UI so he built a tool that lets you DRAW DIRECTLY on the screen claude renders something. if it's wrong you just circle the problem, add an arrow, write a note ai sees your annotations and fixes it way faster than describing it by text AND it's free + open source

Om Patel

108,984 görüntüleme • 4 ay önce