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You cannot really train all these models to cater to different preferences. Can you have one model that caters to all? Furong Huang unveils a technique to customize AI models on-the-fly to user goals, reducing the computational cost of tailoring AI systems to individual needs.

FAR.AI

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410,541 views • 1 year ago •via X (Twitter)

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FAR.AI1 year ago

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Ansem explains the thesis behind Dolphin AI “Dolphin is the provider of the uncensored model that Venice uses” “ChatGPT, Claude, all of the big labs have very strict rules on what you can ask the models, they’re censored very heavily” “We’re not in control of what that censorship is and what they’re telling the models not to say” “One of crypto’s core tenets is not having to rely on some centralized entity deciding what you can and cannot do” “They want the technology to be free and open to everyone. Uncensored models are one way crypto is looking at doing that”

Ansem explains the thesis behind Dolphin AI “Dolphin is the provider of the uncensored model that Venice uses” “ChatGPT, Claude, all of the big labs have very strict rules on what you can ask the models, they’re censored very heavily” “We’re not in control of what that censorship is and what they’re telling the models not to say” “One of crypto’s core tenets is not having to rely on some centralized entity deciding what you can and cannot do” “They want the technology to be free and open to everyone. Uncensored models are one way crypto is looking at doing that”

Market Bubble

20,688 views • 1 month ago

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

As AI labs race to train and deploy new frontier models, existing models become more affordable with better tokenomics. ✨ "Everybody's trying to get to the next frontier. And every time they get to the next frontier, the last generation AI tokens, the cost starts to decline about a factor of 10x every year," said NVIDIA CEO Jensen Huang in a recent keynote. Model optimization techniques such as speculative decoding and multi-token prediction, combined with inference serving platforms like NVIDIA Dynamo on NVIDIA Blackwell NVL72 systems, enable AI factories to boost throughput by 10x with one-tenth of the cost per token. Learn more about AI factory tokenomics ➡️

NVIDIA AI

16,053 views • 5 months ago

Big Tech don’t want you realizing how valuable your data really is to AI. Every click. Every prompt. Every online action. All helping train AI models worth trillions. Yet the people creating that value get almost nothing in return. Even though it’s our data making these systems smarter every single day. At Action Model, we believe in a different future. One where the people helping train AI can earn rewards and ownership in the value they create. That’s exactly what we’re building. 400,000+ people have already joined the movement. Have you?

Big Tech don’t want you realizing how valuable your data really is to AI. Every click. Every prompt. Every online action. All helping train AI models worth trillions. Yet the people creating that value get almost nothing in return. Even though it’s our data making these systems smarter every single day. At Action Model, we believe in a different future. One where the people helping train AI can earn rewards and ownership in the value they create. That’s exactly what we’re building. 400,000+ people have already joined the movement. Have you?

Action Model

27,932 views • 23 days ago

1/ Excited to launch an experiment today - introducing Window, a way to use your own AI models on the web - including local ones! It's a bet on a new kind of AI app emerging, one that shifts model authentication and management to the user.

1/ Excited to launch an experiment today - introducing Window, a way to use your own AI models on the web - including local ones! It's a bet on a new kind of AI app emerging, one that shifts model authentication and management to the user.

Alex Atallah

389,206 views • 3 years ago

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 views • 2 years ago

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 views • 1 year ago

Mistral CEO Arthur Mensch on the need for open-source AI: “If you assume that the entire economy is going to run on AI systems, enterprises will just want to make sure that nobody can turn off their systems.” “If you treat intelligence as electricity, then you just want to make sure that your access to intelligence cannot be throttled.” "The only way in which you can create systems that are effectively using the folklore knowledge of your employees, the knowledge that you've accrued for decades... is to create your own models based on those open-source models." “This is a technology which is so important, that you don’t want to be locked into a single vendor.” Arthur Mensch on Big Technology Podcast

Mistral CEO Arthur Mensch on the need for open-source AI: “If you assume that the entire economy is going to run on AI systems, enterprises will just want to make sure that nobody can turn off their systems.” “If you treat intelligence as electricity, then you just want to make sure that your access to intelligence cannot be throttled.” "The only way in which you can create systems that are effectively using the folklore knowledge of your employees, the knowledge that you've accrued for decades... is to create your own models based on those open-source models." “This is a technology which is so important, that you don’t want to be locked into a single vendor.” Arthur Mensch on Big Technology Podcast

a16z

287,920 views • 4 months ago

Reminder that Ollama can serve AI models to your whole house. Just expose it to the network with OLLAMA_HOST. Access all your AI models over your network — for free.

Reminder that Ollama can serve AI models to your whole house. Just expose it to the network with OLLAMA_HOST. Access all your AI models over your network — for free.

Aaron Ng

109,007 views • 1 year ago

Say hi to Enzo, the AI with a heart. The first true creative agent to help you with all your creative needs. From generating images and videos, finetuning models, to creating marketing content, Enzo is your all in one teammate. Only on EverArt ✨

Say hi to Enzo, the AI with a heart. The first true creative agent to help you with all your creative needs. From generating images and videos, finetuning models, to creating marketing content, Enzo is your all in one teammate. Only on EverArt ✨

Pietro Schirano

48,373 views • 1 year ago

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Wide Awake Media

54,084 views • 7 months ago

Larry Ellison—owner of Oracle, CBS, and now TikTok—tells Tony Blair about his plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" his AI models. "We have to take all of this data... and move it into a single, if you will, unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Larry Ellison—owner of Oracle, CBS, and now TikTok—tells Tony Blair about his plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" his AI models. "We have to take all of this data... and move it into a single, if you will, unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Wide Awake Media

144,324 views • 5 months ago

A look at the new Gab AI dashboard. Lightning fast. Every top AI model in one spot. All your chat history across all models in one spot. Create custom AI agents to be whatever you want. We absolutely cooked with this and have much more on the way. 👉

A look at the new Gab AI dashboard. Lightning fast. Every top AI model in one spot. All your chat history across all models in one spot. Create custom AI agents to be whatever you want. We absolutely cooked with this and have much more on the way. 👉

Andrew Torba

54,462 views • 1 year ago

.Anish Acharya says the future of AI isn’t one model to rule them all—and explains why platforms that integrate multiple models will benefit the most: "I think we're going to need and rely on all of the models." "It's sort of like if you have a team of people... if you have five people, they could all do a basic set of things pretty capably." "But then they all have their specializations. Maybe one of them is really good at closing a customer who doesn't want to sign the deal, and one of them is really good at culture and getting the best out of the team." "There are some areas in which they are going to build apps, and that will be a threat to app companies. But there are many areas in which app companies are advantaged. Cursor and Krea are great examples of this—products where you benefit from being multi-model." "When you actually use a creative tool, you don't want to just use Nano Banana, you want to have access to OpenAI, Nano Banana, Kling—all of them—Qwen, you name it. So using a single interface to access all the models is powerful." Anish Acharya on BILLIONS with Guillaume Moubeche

.Anish Acharya says the future of AI isn’t one model to rule them all—and explains why platforms that integrate multiple models will benefit the most: "I think we're going to need and rely on all of the models." "It's sort of like if you have a team of people... if you have five people, they could all do a basic set of things pretty capably." "But then they all have their specializations. Maybe one of them is really good at closing a customer who doesn't want to sign the deal, and one of them is really good at culture and getting the best out of the team." "There are some areas in which they are going to build apps, and that will be a threat to app companies. But there are many areas in which app companies are advantaged. Cursor and Krea are great examples of this—products where you benefit from being multi-model." "When you actually use a creative tool, you don't want to just use Nano Banana, you want to have access to OpenAI, Nano Banana, Kling—all of them—Qwen, you name it. So using a single interface to access all the models is powerful." Anish Acharya on BILLIONS with Guillaume Moubeche

a16z

33,042 views • 3 months ago

꧁IP꧂ encompasses all data used to fuel AI models. On Story, anyone can register open source AI models, data sets, and fine tune their models. AI cannot exist without IP, and Story makes IP programmable.

꧁IP꧂ encompasses all data used to fuel AI models. On Story, anyone can register open source AI models, data sets, and fine tune their models. AI cannot exist without IP, and Story makes IP programmable.

Story

155,404 views • 1 year ago

Satya Nadella predicts, - if you have AI + Quantum Computing, you may use quantum to generate synthetic data - AI can then use that data to train better models for complex fields like chemistry and physics.

Satya Nadella predicts, - if you have AI + Quantum Computing, you may use quantum to generate synthetic data - AI can then use that data to train better models for complex fields like chemistry and physics.

Haider.

78,677 views • 1 year ago

Erik Voorhees explains how you can combine different AI tools to beat any single model "We're working on a concept called Minds. If you put modules of AI systems together the right way, you can get results better than any single model itself" "All the labs are focused on their models and beating each other's scores, but if you combine these things in creative interesting ways you get stuff no model can do on its own" "If no new models came out for five years, the advances in AI would still continue apace because people have not realized how much they can do even with the stuff created two years ago"

Erik Voorhees explains how you can combine different AI tools to beat any single model "We're working on a concept called Minds. If you put modules of AI systems together the right way, you can get results better than any single model itself" "All the labs are focused on their models and beating each other's scores, but if you combine these things in creative interesting ways you get stuff no model can do on its own" "If no new models came out for five years, the advances in AI would still continue apace because people have not realized how much they can do even with the stuff created two years ago"

Market Bubble

15,814 views • 12 days ago

The best place to train AI models. Apply today.

The best place to train AI models. Apply today.

micro1

13,106 views • 2 months ago

This Chinese AI model beat ChatGPT and Gemini Guess what? It's now available on AI Fiesta also. Now you can use up to 7 AI models simultaneously. We are probably the only AI platform in the world where you can do that.

This Chinese AI model beat ChatGPT and Gemini Guess what? It's now available on AI Fiesta also. Now you can use up to 7 AI models simultaneously. We are probably the only AI platform in the world where you can do that.

Dhruv Rathee

1,058,370 views • 7 months ago

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.

paul.nft

32,461 views • 1 year ago

Which model of the 40 models is the right model? The CSIRO can’t say. They just want us to trust them. Apparently they want us to believe that 40 different models can all come to the same conclusion. The only same conclusion I can see is that the taxpayer is ripped off. #auspol

Which model of the 40 models is the right model? The CSIRO can’t say. They just want us to trust them. Apparently they want us to believe that 40 different models can all come to the same conclusion. The only same conclusion I can see is that the taxpayer is ripped off. #auspol

Gerard Rennick

28,624 views • 2 years ago