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“don’t train your own model” is common ai advice. it's wrong. your token bill's the proof. today, we’re excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data. open-weights models are performant and much cheaper. when... trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise. with castform, model training is as simple as prompt engineering. castform bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns. see what you can build with castform👇show more

girish

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456,024 次观看 • 1 个月前 •via X (Twitter)

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Building a truly private AI model isn’t as simple as the tech world wants you to believe. despite all the marketing around one click deployments, RAG systems, fine tuning services, and memory features, we still don’t have models that are genuinely trained only on your personal data with complete privacy from third parties. the reality is that most AI solutions today require you to trust someone else with your books, notes, journals, and training data. even when companies promise privacy, there’s usually a way for the provider or other parties to access your information during training or inference. Phala is taking a different approach by building the infrastructure needed for real data ownership. They’re developing confidential runtime environments where your AI training happens inside secure enclaves with attestation guarantees. The key innovation is their in-enclave keying system through dstack, which means that once your model is training, even Phala themselves cannot see your data or model weights.

Building a truly private AI model isn’t as simple as the tech world wants you to believe. despite all the marketing around one click deployments, RAG systems, fine tuning services, and memory features, we still don’t have models that are genuinely trained only on your personal data with complete privacy from third parties. the reality is that most AI solutions today require you to trust someone else with your books, notes, journals, and training data. even when companies promise privacy, there’s usually a way for the provider or other parties to access your information during training or inference. Phala is taking a different approach by building the infrastructure needed for real data ownership. They’re developing confidential runtime environments where your AI training happens inside secure enclaves with attestation guarantees. The key innovation is their in-enclave keying system through dstack, which means that once your model is training, even Phala themselves cannot see your data or model weights.

soulman 🎮

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A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

Ray Fernando

42,398 次观看 • 4 个月前

Perplexity CEO Aravind Srinivas says the biggest threat to data centers is intelligence that runs locally on your device If models run on your own chip, they can watch your workflow and adapt via "test time training" without data ever leaving your computer "you own it. it's your brain"

Perplexity CEO Aravind Srinivas says the biggest threat to data centers is intelligence that runs locally on your device If models run on your own chip, they can watch your workflow and adapt via "test time training" without data ever leaving your computer "you own it. it's your brain"

Haider.

855,176 次观看 • 6 个月前

Right now, almost everything you do online feeds centralized AI models. Reddit posts helped train large language models. Instagram photos power image generation. Now your daily online activity is being used to automate work itself. Action Model is pushing back with a community-owned AI model, where users stay in control and own what they help train. - Train passively or guide it step-by-step - Private data stays masked and in your control - Review, edit, or delete training anytime This isn’t a vision, it’s the standard the future of AI should be built on.

Right now, almost everything you do online feeds centralized AI models. Reddit posts helped train large language models. Instagram photos power image generation. Now your daily online activity is being used to automate work itself. Action Model is pushing back with a community-owned AI model, where users stay in control and own what they help train. - Train passively or guide it step-by-step - Private data stays masked and in your control - Review, edit, or delete training anytime This isn’t a vision, it’s the standard the future of AI should be built on.

Action Model

80,918 次观看 • 5 个月前

New short course on Fine-tuning LLMs! Many developers are moving beyond only prompting, to also fine-tuning LLMs - that is, taking a pre-trained model and training it further on your own data, which can deliver superior results inexpensively. In this course, Sharon Zhou, CEO of Lamini (disclosure: I’m a minor shareholder) shows you how to recognize when fine-tuning can be help, and how to train an open-source LLM on your own data. I hope you enjoy the course!

New short course on Fine-tuning LLMs! Many developers are moving beyond only prompting, to also fine-tuning LLMs - that is, taking a pre-trained model and training it further on your own data, which can deliver superior results inexpensively. In this course, Sharon Zhou, CEO of Lamini (disclosure: I’m a minor shareholder) shows you how to recognize when fine-tuning can be help, and how to train an open-source LLM on your own data. I hope you enjoy the course!

Andrew Ng

502,821 次观看 • 2 年前

Mureka vs. Suno Suno might be a popular choice, but gives you way more control and creative flexibility. With Mureka, you can: • Use audio as a prompt, not just text • Keep the vocals consistent throughout your track • Train your own custom model • Access an open API to build or connect it with other tools

Mureka vs. Suno Suno might be a popular choice, but gives you way more control and creative flexibility. With Mureka, you can: • Use audio as a prompt, not just text • Keep the vocals consistent throughout your track • Train your own custom model • Access an open API to build or connect it with other tools

Nelly;

29,420 次观看 • 1 年前

Alex Karp went on TV and said his enterprise customers are done letting AI companies quietly steal the alpha out of their business, and the CEO of Together AI explained on CNBC exactly how that theft works. Karp said his customers are livid. They feel they pay for tokens that create nothing while handing over the secret sauce of their own company. Then Vipul Ved Prakash laid out the mechanism. Every time you send your data to a company with very smart models, you hand them the recipe to rebuild what you do. Your data is the thing that makes you money. And you are pushing it straight into a competitor's system. The smarter the models get, the more they learn from what you feed them. This is why companies are moving to open models, and it was never really about price. You can download an open model, run it inside your own walls, and never let your data leave the building. It scores 81 against the top closed model's 85, and runs up to sixty times cheaper. Half the Fortune 500 is already running open models from Hugging Face. The story was that the labs win because they own the best models. The companies paying them just realized they owned the best data the whole time.

Alex Karp went on TV and said his enterprise customers are done letting AI companies quietly steal the alpha out of their business, and the CEO of Together AI explained on CNBC exactly how that theft works. Karp said his customers are livid. They feel they pay for tokens that create nothing while handing over the secret sauce of their own company. Then Vipul Ved Prakash laid out the mechanism. Every time you send your data to a company with very smart models, you hand them the recipe to rebuild what you do. Your data is the thing that makes you money. And you are pushing it straight into a competitor's system. The smarter the models get, the more they learn from what you feed them. This is why companies are moving to open models, and it was never really about price. You can download an open model, run it inside your own walls, and never let your data leave the building. It scores 81 against the top closed model's 85, and runs up to sixty times cheaper. Half the Fortune 500 is already running open models from Hugging Face. The story was that the labs win because they own the best models. The companies paying them just realized they owned the best data the whole time.

Ihtesham Ali

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🚀 Discover PublicAI Product Ecosystem – Your Gateway to contributes data to train AI and share revenues! 🚀 Unleash the power of PublicAI's Data Hunter Chrome Extension and transform your social media browsing into a treasure trove of valuable data. 📊 Upload your ChatGPT conversations with a simple click and earn rewards for your valuable contributions. 🔍 Use Data Hub to harness your intuition and refine data through community consensus. 🤖 Harness community-approved data to train AI models and craft your own AI Agent. Embark on the AI journey with PublicAI and redefine the future of AI interaction! 🌐🤖 #PublicAI

🚀 Discover PublicAI Product Ecosystem – Your Gateway to contributes data to train AI and share revenues! 🚀 Unleash the power of PublicAI's Data Hunter Chrome Extension and transform your social media browsing into a treasure trove of valuable data. 📊 Upload your ChatGPT conversations with a simple click and earn rewards for your valuable contributions. 🔍 Use Data Hub to harness your intuition and refine data through community consensus. 🤖 Harness community-approved data to train AI models and craft your own AI Agent. Embark on the AI journey with PublicAI and redefine the future of AI interaction! 🌐🤖 #PublicAI

PublicAI

19,805 次观看 • 2 年前

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.

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 次观看 • 2 年前

📢 New in Visual Studio: Bring Your Own Model to Chat We’re expanding flexibility in Copilot for Visual Studio. You can now connect API keys from providers like OpenAI, Anthropic, and Google to run Chat on the model of your choice. This update gives you: ✅ Choice – Access the latest models beyond the built-in defaults. ✅ Customization – Select models that fit your infrastructure and requirements. ✅ Control – Manage API usage, limits, and billing directly with your provider. ✅ Flexibility – Switch seamlessly between Copilot’s built-in models and your own. This release is the first step toward a more open and customizable chat experience in Visual Studio, with more providers and capabilities to come. 🔗

📢 New in Visual Studio: Bring Your Own Model to Chat We’re expanding flexibility in Copilot for Visual Studio. You can now connect API keys from providers like OpenAI, Anthropic, and Google to run Chat on the model of your choice. This update gives you: ✅ Choice – Access the latest models beyond the built-in defaults. ✅ Customization – Select models that fit your infrastructure and requirements. ✅ Control – Manage API usage, limits, and billing directly with your provider. ✅ Flexibility – Switch seamlessly between Copilot’s built-in models and your own. This release is the first step toward a more open and customizable chat experience in Visual Studio, with more providers and capabilities to come. 🔗

Visual Studio

11,873 次观看 • 11 个月前

Tired of generic, hallucinating LLMs? We use AI for almost everything these days, whether it’s helping with math homework or looking up remedies when we’re sick. It's become part of our daily routines. Now imagine if Einstein helped you solve that homework, or Eminem wrote the lyrics for your next rap song. What if your AI was built specifically for your world? Trained on your domain, your data, you become the master of it. Introducing OpenLedger AI Studio. A powerful platform to build and use specialized models with built-in explainability and attribution. Fine-tune using your data through our Model Factory, and deploy them seamlessly. Your models won’t just reason - With OpenLedger, you can explain every decision, trace its data, and reward contributors. Truly Open, Verified, and Explainable AI begins here.

Tired of generic, hallucinating LLMs? We use AI for almost everything these days, whether it’s helping with math homework or looking up remedies when we’re sick. It's become part of our daily routines. Now imagine if Einstein helped you solve that homework, or Eminem wrote the lyrics for your next rap song. What if your AI was built specifically for your world? Trained on your domain, your data, you become the master of it. Introducing OpenLedger AI Studio. A powerful platform to build and use specialized models with built-in explainability and attribution. Fine-tune using your data through our Model Factory, and deploy them seamlessly. Your models won’t just reason - With OpenLedger, you can explain every decision, trace its data, and reward contributors. Truly Open, Verified, and Explainable AI begins here.

OpenLedger

172,497 次观看 • 1 年前

Palantir CEO Alex Karp on why the model is not the moat. The application layer is: "To make them valuable in an enterprise, like a battlefield, regulated, or manufacturing context, you have to have what's called an application layer." "We have this thing called ontology that now everyone's copying. It takes a large language model and makes it safe and useful and precise." "Safe because it doesn't touch your underlying data." "Safe because it prevents the large language model from caching your data and replicating your business." The tell for any enterprise AI pitch: ask what sits between the model and your data. If the answer is nothing, you are not buying leverage. You are renting a fast way to hand your business to whoever owns the weights. Alex Karp, CEO of Palantir, on CNBC.

Palantir CEO Alex Karp on why the model is not the moat. The application layer is: "To make them valuable in an enterprise, like a battlefield, regulated, or manufacturing context, you have to have what's called an application layer." "We have this thing called ontology that now everyone's copying. It takes a large language model and makes it safe and useful and precise." "Safe because it doesn't touch your underlying data." "Safe because it prevents the large language model from caching your data and replicating your business." The tell for any enterprise AI pitch: ask what sits between the model and your data. If the answer is nothing, you are not buying leverage. You are renting a fast way to hand your business to whoever owns the weights. Alex Karp, CEO of Palantir, on CNBC.

Karl Mehta

31,891 次观看 • 24 天前

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Santiago

39,101 次观看 • 2 年前

Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by South Park Commons to share how companies are owning their intelligence with Applied Compute.

Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by South Park Commons to share how companies are owning their intelligence with Applied Compute.

Applied Compute

118,922 次观看 • 1 个月前

Today, AWS CEO Matt Garman announced Nova Forge, a model builder which lets companies inject their own data during the pre-training phase. "You [tell Forge]: 'Here's my corpus of corporate data, here's everything I need to know about my industry.' We then mix that in and finish pre-training the model. So you get a pre-trained model that totally understands your company and your data."

Today, AWS CEO Matt Garman announced Nova Forge, a model builder which lets companies inject their own data during the pre-training phase. "You [tell Forge]: 'Here's my corpus of corporate data, here's everything I need to know about my industry.' We then mix that in and finish pre-training the model. So you get a pre-trained model that totally understands your company and your data."

TBPN

97,010 次观看 • 7 个月前

David Friedberg says Anthropic asked big pharma for their data and nearly everyone said no "There's been an effort by Anthropic to sign up life sciences companies to contribute to a new life sciences focused model. They're approaching these large companies with large proprietary data sets and saying, if you share your data, we will give you early access, some sort of proprietary value. Sign this NDA and you can participate with us." "I think nearly everyone I've spoken with has woken up to the fact that they are trying to commoditize everyone's business. If all of the tens of billions of dollars you have invested in experiments and product development, and you've generated all of this proprietary data along the way, that data is a true asset of your organization. It's an asset that you've spent billions of dollars developing." "And by handing it over to a model company to then combine with other people's data, you are commoditizing the one core differentiation that you have. And so everyone is largely saying no." "I think what everyone's realizing is they're better off developing their own weights and their own models using either an open source basis or there might be some intermediary business model that evolves."

David Friedberg says Anthropic asked big pharma for their data and nearly everyone said no "There's been an effort by Anthropic to sign up life sciences companies to contribute to a new life sciences focused model. They're approaching these large companies with large proprietary data sets and saying, if you share your data, we will give you early access, some sort of proprietary value. Sign this NDA and you can participate with us." "I think nearly everyone I've spoken with has woken up to the fact that they are trying to commoditize everyone's business. If all of the tens of billions of dollars you have invested in experiments and product development, and you've generated all of this proprietary data along the way, that data is a true asset of your organization. It's an asset that you've spent billions of dollars developing." "And by handing it over to a model company to then combine with other people's data, you are commoditizing the one core differentiation that you have. And so everyone is largely saying no." "I think what everyone's realizing is they're better off developing their own weights and their own models using either an open source basis or there might be some intermediary business model that evolves."

dnap

286,036 次观看 • 23 天前

Do you have a visualiser in your cupboard? Plug it into your computer and see how you can bring the teaching slides to life through live modelling! 🎬 By using the visualiser, you can model your thinking aloud and children can build alongside you. It also gives you the flexibility to model as many examples as you may need! 🟡🔴

Do you have a visualiser in your cupboard? Plug it into your computer and see how you can bring the teaching slides to life through live modelling! 🎬 By using the visualiser, you can model your thinking aloud and children can build alongside you. It also gives you the flexibility to model as many examples as you may need! 🟡🔴

White Rose Education

20,961 次观看 • 1 年前

Stop hardcoding one model name in your code. Now you can give each request a policy: a small rule for what the call needs It picks the right model for the job, on your own keys This is unhardcoded, our new open source routing for AI models, live today

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GenLayer

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Create your own local and free AI agent using an open source model You can combine: - IBM's Granite 3.3 8B AI model - LM Studio to run it on a laptop - Smolagents to build your agent Small AI models are now powerful enough to run an autonomous agent. Thanks to IBM France for sponsoring this content!

Create your own local and free AI agent using an open source model You can combine: - IBM's Granite 3.3 8B AI model - LM Studio to run it on a laptop - Smolagents to build your agent Small AI models are now powerful enough to run an autonomous agent. Thanks to IBM France for sponsoring this content!

Paul Couvert

18,890 次观看 • 1 年前

Agentic trading is now live on Gemini 🤖 Connect your AI model of choice directly to the exchange, train it with plain language, and run your own trading systems directly on the exchange Built to get you trading in minutes

Agentic trading is now live on Gemini 🤖 Connect your AI model of choice directly to the exchange, train it with plain language, and run your own trading systems directly on the exchange Built to get you trading in minutes

Gemini

166,245 次观看 • 3 个月前