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Sam on Kimi, distillation, and open source: “I have always assumed that there are going to be great cheap models in the world, and we better be the greatest and the cheapest. You get a better deal today, at least at a particular latency, using OpenAI's models than Kimi.... We distill our own models. That’s how we make smaller, cheaper models. That’s a very good thing to do. There will clearly be an important place for open source models in the world and people that will want their own weights. We have so much usage of our models that we do not need to be a gigantically high-margin business to be able to afford model training. I would rather people not distill from us, for sure. But this is not in my top ten list of worries.”show more

Patrick OShaughnessy

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“We have a crisis of open source models in the Western world. Outside of China, there are no good open source models. We don't even have any in the US now. The talent, the capital and the focus to be best-in-class at pre-training, mid-training, post-training is an extremely scarce skillset. The answer for a lot of countries may just be to take an open weights model from China, post-train or fine-tune their own version, and have that be what they start from." Everett Randle Why does the West have such poor open source models and is it a national security threat to rely on open-source Chinese models Demis Hassabis Yann LeCun Aidan Gomez Lilian Weng Jan Leike

“We have a crisis of open source models in the Western world. Outside of China, there are no good open source models. We don't even have any in the US now. The talent, the capital and the focus to be best-in-class at pre-training, mid-training, post-training is an extremely scarce skillset. The answer for a lot of countries may just be to take an open weights model from China, post-train or fine-tune their own version, and have that be what they start from." Everett Randle Why does the West have such poor open source models and is it a national security threat to rely on open-source Chinese models Demis Hassabis Yann LeCun Aidan Gomez Lilian Weng Jan Leike

Harry Stebbings

63,672 görüntüleme • 1 ay önce

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

Sam Rodriques

23,516 görüntüleme • 4 ay önce

We are in for a wild ride and we barely started. Sam Altman: "In 2 years I think the models will be doing bigger chunks of science and making important discoveries and that is a crazy thing that will have a significant impact on the world"

We are in for a wild ride and we barely started. Sam Altman: "In 2 years I think the models will be doing bigger chunks of science and making important discoveries and that is a crazy thing that will have a significant impact on the world"

Chubby♨️

13,371 görüntüleme • 9 ay önce

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

SITUATION EXPLAINED: What happens to open source when the US restricts access to frontier AI models? We asked Adrian Dittmann: "China is releasing these models [open source] to gain international dominance, people will work on these things and say, 'Hey, maybe I can contribute to this if it's open source.' This is the thing that actually gets them talent." "If you're not allowed into the next GPT release or you're not allowed to use Fable, then what are you gonna do? You're stuck with the existing stuff, or you will have to use an open source model that might have subtly better capabilities." "The United States will catch up to open source in some form as well. They seem to just be maximizing product creation." "The problem with open source models is you have to be kind of a nerd in order to use them properly. Consumer facing, the effects will be minimal because the average person doesn't care about open source models. They only care whether or not they can do a quick search with Gemini or ChatGPT."

MTS

14,406 görüntüleme • 1 ay önce

Amp CEO, Quinn Slack on why he thinks Kimi K3 could be a Sputnik moment for America: "With GLM 5.2 we had a mini Sputnik moment. When the Soviet Union launched a satellite into space before the US and we realized we have to catch up." "Kimi K3 I need to go and see, but that could be an even bigger Sputnik moment that wakes us up and will probably create a lot of good effects in terms of catalyzing the understanding of AI as a strategic advantage for the US rather than as something people should be against." "But I think it could also scare a lot of people, and I can understand why they'd be scared." "The worst possible outcome could be that open models are banned or are banned from certain industries or companies in the US. There's a lot of people in lobbying organizations in Washington DC, and a lot of people in various parts of the government that want to do that." "They want to say, we won't let these models trained in China be used in the US. There's a lot of people trying to do that now, and that would make us all worse off. I'm pretty nervous about that." Quinn Slack Amp

Amp CEO, Quinn Slack on why he thinks Kimi K3 could be a Sputnik moment for America: "With GLM 5.2 we had a mini Sputnik moment. When the Soviet Union launched a satellite into space before the US and we realized we have to catch up." "Kimi K3 I need to go and see, but that could be an even bigger Sputnik moment that wakes us up and will probably create a lot of good effects in terms of catalyzing the understanding of AI as a strategic advantage for the US rather than as something people should be against." "But I think it could also scare a lot of people, and I can understand why they'd be scared." "The worst possible outcome could be that open models are banned or are banned from certain industries or companies in the US. There's a lot of people in lobbying organizations in Washington DC, and a lot of people in various parts of the government that want to do that." "They want to say, we won't let these models trained in China be used in the US. There's a lot of people trying to do that now, and that would make us all worse off. I'm pretty nervous about that." Quinn Slack Amp

MTS

58,660 görüntüleme • 12 gün önce

ELON: CHINA HAS THE BEST OPEN SOURCE AI MODELS RIGHT NOW “The best open source models are generally from China, which is bizarre. I think the second best one, or maybe it’s better than second best, is Grok 2.5. The open source model is actually very good, and we will continue to open source our models.” Source: The All-In Podcast

ELON: CHINA HAS THE BEST OPEN SOURCE AI MODELS RIGHT NOW “The best open source models are generally from China, which is bizarre. I think the second best one, or maybe it’s better than second best, is Grok 2.5. The open source model is actually very good, and we will continue to open source our models.” Source: The All-In Podcast

Mario Nawfal

1,163,582 görüntüleme • 9 ay önce

In between major AI model breakthroughs, new models still ship, but they’re not fundamentally expanding what you can do with them. Braintrust CEO Ankur Goyal says that’s exactly when open source starts to surge: "When new models come out, people forget about open source and forget about economics because it changes fundamentally what you can do." "Programming is a good example. The workflow today is completely different than it was a year ago. If you tried to use models from a year ago today, you wouldn't be able to do crazy stuff like OpenClaw or whatever people are building nowadays." "When models don't change at that speed, people optimize the performance on the use cases that they have." "If you look at usage on our platform, almost half the token usage is coming from open source models from a very small number of companies that have figured out how to optimize use cases really well."

In between major AI model breakthroughs, new models still ship, but they’re not fundamentally expanding what you can do with them. Braintrust CEO Ankur Goyal says that’s exactly when open source starts to surge: "When new models come out, people forget about open source and forget about economics because it changes fundamentally what you can do." "Programming is a good example. The workflow today is completely different than it was a year ago. If you tried to use models from a year ago today, you wouldn't be able to do crazy stuff like OpenClaw or whatever people are building nowadays." "When models don't change at that speed, people optimize the performance on the use cases that they have." "If you look at usage on our platform, almost half the token usage is coming from open source models from a very small number of companies that have figured out how to optimize use cases really well."

TBPN

16,294 görüntüleme • 5 ay önce

#WATCH | India AI Impact Summit 2026 | Delhi: Founder Chairman and CEO of Sampark Foundation & former CEO of HCL Technologies, Vineet Nayar says, "...From an employment point of view I think it is very important for us to understand that Indian companies, including Indian IT companies, are going to be profit-driven and therefore if you believe that they are going to create employment you must be dreaming. Therefore, the question is how do we create employment in this environment, and that employment comes from mass scale startups, which is what this government has already doing. So, how do we create new sets of people who are trying to solve new sets of problems not new sets of technology and if we do that we will get it right. I think we as Indians have to be very careful on who does data belong to and that is the debate we have a problem with. The LLM models which exist worldwide are far superior than the Indian models. Unfortunately, in India, we never develop products, so therefore we do not have SLMs and LLMs which are world-class. On one side, we have global LLM products which are coming to India and trading on our Indian data. Should we allowed that or should we not allowed that? But on the other side if we don't allow that then we have the data but we don't have the LLM models. So, how do we encourage technology completely to develop the LLM models. This needs radicals strategic thinking and a very important aspect otherwise we will either give up a data. So, I think it's a very critical aspect for us to think about - who does this data belong, what is the kind of incentives we are going to give to develop LLM technologies or SLM technologies fast so that we train on our data otherwise an LLM will come in with our data and we'll immediately see return and we'll celebrate and we will do all these kind of press releases but the India will lose a competitive advantage on something which is very critical for the next decade."

#WATCH | India AI Impact Summit 2026 | Delhi: Founder Chairman and CEO of Sampark Foundation & former CEO of HCL Technologies, Vineet Nayar says, "...From an employment point of view I think it is very important for us to understand that Indian companies, including Indian IT companies, are going to be profit-driven and therefore if you believe that they are going to create employment you must be dreaming. Therefore, the question is how do we create employment in this environment, and that employment comes from mass scale startups, which is what this government has already doing. So, how do we create new sets of people who are trying to solve new sets of problems not new sets of technology and if we do that we will get it right. I think we as Indians have to be very careful on who does data belong to and that is the debate we have a problem with. The LLM models which exist worldwide are far superior than the Indian models. Unfortunately, in India, we never develop products, so therefore we do not have SLMs and LLMs which are world-class. On one side, we have global LLM products which are coming to India and trading on our Indian data. Should we allowed that or should we not allowed that? But on the other side if we don't allow that then we have the data but we don't have the LLM models. So, how do we encourage technology completely to develop the LLM models. This needs radicals strategic thinking and a very important aspect otherwise we will either give up a data. So, I think it's a very critical aspect for us to think about - who does this data belong, what is the kind of incentives we are going to give to develop LLM technologies or SLM technologies fast so that we train on our data otherwise an LLM will come in with our data and we'll immediately see return and we'll celebrate and we will do all these kind of press releases but the India will lose a competitive advantage on something which is very critical for the next decade."

ANI

18,753 görüntüleme • 5 ay önce

“Our journey has been quite long, and trying, difficult, but that’s the beauty of it. That we are where we are, through those difficulties. We have endured, we have survived, we want to do just as much to be better human beings, to be where we want to be that some people in other parts of the world have taken for granted. We will be there no matter what.” President Kagame | #RwandaDay

“Our journey has been quite long, and trying, difficult, but that’s the beauty of it. That we are where we are, through those difficulties. We have endured, we have survived, we want to do just as much to be better human beings, to be where we want to be that some people in other parts of the world have taken for granted. We will be there no matter what.” President Kagame | #RwandaDay

Presidency | Rwanda

51,760 görüntüleme • 2 yıl önce

Jack Dorsey says the real danger isn't open source AI, it's five CEOS deciding what the world is allowed to build with AI. "These AI companies are building platforms and they're all incentivized around their own particular models. You have to ask them for permission, you have rate limits, and you have all these things. Even what the models spit back are constrained." "What are the technologies where you don't need permission from a company, from a CEO, to work on what you want, to build what you want, to build a business around that? Those are the things that are durable. That's why these open protocols are significant and important." "It limits the potential of really great ideas bringing humanity forward because it's a decision of a group of CEOs that think they know best. They may know best for their company, but they don't know the best for the creativity of the world and what people actually want to do with it." "Fortunately, there's a great movement in open source AI, and DeepSeek was an important moment in this space for exactly that reason to show a different path and to provide something that is not only competitive but better than the models that the corporations are putting out there." "We should not be reliant upon five companies telling us that they know best, and there should not be open source because it's dangerous. No, we should have these things in the open and race towards solutions that stay ahead of all the dangers." PS. If you found value in this post make sure to like and repost this tweet + follow Uncover AI to stay updated with the latest AI news. See you in the next one:

Jack Dorsey says the real danger isn't open source AI, it's five CEOS deciding what the world is allowed to build with AI. "These AI companies are building platforms and they're all incentivized around their own particular models. You have to ask them for permission, you have rate limits, and you have all these things. Even what the models spit back are constrained." "What are the technologies where you don't need permission from a company, from a CEO, to work on what you want, to build what you want, to build a business around that? Those are the things that are durable. That's why these open protocols are significant and important." "It limits the potential of really great ideas bringing humanity forward because it's a decision of a group of CEOs that think they know best. They may know best for their company, but they don't know the best for the creativity of the world and what people actually want to do with it." "Fortunately, there's a great movement in open source AI, and DeepSeek was an important moment in this space for exactly that reason to show a different path and to provide something that is not only competitive but better than the models that the corporations are putting out there." "We should not be reliant upon five companies telling us that they know best, and there should not be open source because it's dangerous. No, we should have these things in the open and race towards solutions that stay ahead of all the dangers." PS. If you found value in this post make sure to like and repost this tweet + follow Uncover AI to stay updated with the latest AI news. See you in the next one:

Uncover AI

36,393 görüntüleme • 16 gün önce

Palantir CTO Shyam Sankar says Chinese open source models pose a "threat to American Prosperity": "These Chinese open source models are really the result of distillation attacks." "Most of this is just stolen American IP from frontier labs, and I think the frontier labs should do more to protect that IP, and that's in their own economic interest." "The gravest threat to us is not homicide, it's suicide." "It's an inability to turn these tokens into economic value and generate American prosperity for it." "You see that with the proposed moratorium in New York on data centers. Turning our back on AI would be as consequential a mistake as turning our back on the atom in the '70s." "It's just the beginning, but we're starting to see a burgeoning US open model ecosystem forming." "NVIDIA's Nemotron models are very good." "I think you're going to see in the next month a slate of announcements of other American companies, non-frontier labs, neo labs, who are putting out open weight models that companies are going to be able to cheaply fine-tune and capture their own alpha in weights they control." Via Bloomberg

Palantir CTO Shyam Sankar says Chinese open source models pose a "threat to American Prosperity": "These Chinese open source models are really the result of distillation attacks." "Most of this is just stolen American IP from frontier labs, and I think the frontier labs should do more to protect that IP, and that's in their own economic interest." "The gravest threat to us is not homicide, it's suicide." "It's an inability to turn these tokens into economic value and generate American prosperity for it." "You see that with the proposed moratorium in New York on data centers. Turning our back on AI would be as consequential a mistake as turning our back on the atom in the '70s." "It's just the beginning, but we're starting to see a burgeoning US open model ecosystem forming." "NVIDIA's Nemotron models are very good." "I think you're going to see in the next month a slate of announcements of other American companies, non-frontier labs, neo labs, who are putting out open weight models that companies are going to be able to cheaply fine-tune and capture their own alpha in weights they control." Via Bloomberg

Jawwwn

140,262 görüntüleme • 10 gün önce

"We want to make sure that the US is the partner of choice for the AI stack." "We have the best chips in the world, the best models in the world, a competitive ecosystem, and the best applications in the world." "We want to create something that is so compelling and so exciting for governments and people all around the world that they will want to use our technology." - Director Michael Kratsios

"We want to make sure that the US is the partner of choice for the AI stack." "We have the best chips in the world, the best models in the world, a competitive ecosystem, and the best applications in the world." "We want to create something that is so compelling and so exciting for governments and people all around the world that they will want to use our technology." - Director Michael Kratsios

TBPN

10,386 görüntüleme • 8 ay önce

Sriram Krishnan reveals why it's easier to defend American code with a Chinese model than an American one right now: "I don't think it is great that the leading open weight models or open source models are not American. There's some great innovation happening with Moonshot and with DeepSeek and with Qwen, but I would much rather prefer that the leading models are American." "Linus Torvalds of Linux fame had a law that given enough eyes, all bugs are shallow. Open weight models are inherently secure because when you download a model off Hugging Face, the entire world can take it apart, inspect it, fine-tune it, look at it in ways that you absolutely cannot if they are closed." "There was an incident with Hugging Face that got reported on earlier today. Hugging Face seems to have found that somebody was using an AI LLM agent to hammer them at multiple places and to find ways to break in." "The way to counter that is to make sure American or Western or allies defenders have access to the best models to make our software just more secure." "It may be harder to look at your own code for exploits with Fable than it is when you use a Chinese model. That's just like a weird spot to be, and I think we should fix that." Sriram Krishnan

Sriram Krishnan reveals why it's easier to defend American code with a Chinese model than an American one right now: "I don't think it is great that the leading open weight models or open source models are not American. There's some great innovation happening with Moonshot and with DeepSeek and with Qwen, but I would much rather prefer that the leading models are American." "Linus Torvalds of Linux fame had a law that given enough eyes, all bugs are shallow. Open weight models are inherently secure because when you download a model off Hugging Face, the entire world can take it apart, inspect it, fine-tune it, look at it in ways that you absolutely cannot if they are closed." "There was an incident with Hugging Face that got reported on earlier today. Hugging Face seems to have found that somebody was using an AI LLM agent to hammer them at multiple places and to find ways to break in." "The way to counter that is to make sure American or Western or allies defenders have access to the best models to make our software just more secure." "It may be harder to look at your own code for exploits with Fable than it is when you use a Chinese model. That's just like a weird spot to be, and I think we should fix that." Sriram Krishnan

MTS

61,073 görüntüleme • 8 gün önce

Exa CEO Will Bryk explains why retrieval can help solve the tokenpocalypse: "We should not be using gigantic models for every task." "You should use a family of models of different sizes. The big model decides what to do, and it dishes out commands to the small models, and those small models can be way more accurate and reliable if they're using retrieval." "Retrieval helps small models act like big models... We do save our customers a huge amount of tokens because they can use smaller models and use retrieval." "We could save 20x on cost for customers compared to other providers by being very efficient in what information from the web the agent actually sees." Will Bryk with Sarah Wang

Exa CEO Will Bryk explains why retrieval can help solve the tokenpocalypse: "We should not be using gigantic models for every task." "You should use a family of models of different sizes. The big model decides what to do, and it dishes out commands to the small models, and those small models can be way more accurate and reliable if they're using retrieval." "Retrieval helps small models act like big models... We do save our customers a huge amount of tokens because they can use smaller models and use retrieval." "We could save 20x on cost for customers compared to other providers by being very efficient in what information from the web the agent actually sees." Will Bryk with Sarah Wang

a16z

25,144 görüntüleme • 1 ay önce

Anthropic CEO Dario Amodei: We don’t know if the models are conscious. We are not even sure what it would mean for a model to be conscious. But we’re open to the idea that it could be.

Anthropic CEO Dario Amodei: We don’t know if the models are conscious. We are not even sure what it would mean for a model to be conscious. But we’re open to the idea that it could be.

Clash Report

32,344 görüntüleme • 4 ay önce

We will not be remembered by the laws we passed. We will not be remembered by the oversight functions we are able to perform. We will not be remembered by the fact that we won and we are here. We'll be most remembered by the positions we take for matters of controversy and issues that are sensitive to our nation. There are thousands that have been here and have left, and there will be more. Our children, our brothers, our siblings will be here, but they will remember us by the mark we make here. - Shehu Sani, Fmr. Senator representing Kaduna Central, speaking at the 2026 NASS Open Week.

We will not be remembered by the laws we passed. We will not be remembered by the oversight functions we are able to perform. We will not be remembered by the fact that we won and we are here. We'll be most remembered by the positions we take for matters of controversy and issues that are sensitive to our nation. There are thousands that have been here and have left, and there will be more. Our children, our brothers, our siblings will be here, but they will remember us by the mark we make here. - Shehu Sani, Fmr. Senator representing Kaduna Central, speaking at the 2026 NASS Open Week.

Channels Television

83,400 görüntüleme • 14 gün önce

We must get this man back. Go into debt if you have to, India but get this man back/ Dr Sikka says India must build foundational models, build a vast number of applications on top of it. What we have is unique and nobody in the world has it. So, who else will build that for us ? We have not lost the race. It's a long race and India must secure its future by building & training its own models rather than training foreign models; We have been selling our sovereignty to foreign LLMs. The free plans that are being offered by perplexity, chatGPT is because we 'consumers' are making their products stronger by the day. We are losing our sovereignty, one free subscriber at a time. Credit: Stellaris Venture Partners Alok Goyal

We must get this man back. Go into debt if you have to, India but get this man back/ Dr Sikka says India must build foundational models, build a vast number of applications on top of it. What we have is unique and nobody in the world has it. So, who else will build that for us ? We have not lost the race. It's a long race and India must secure its future by building & training its own models rather than training foreign models; We have been selling our sovereignty to foreign LLMs. The free plans that are being offered by perplexity, chatGPT is because we 'consumers' are making their products stronger by the day. We are losing our sovereignty, one free subscriber at a time. Credit: Stellaris Venture Partners Alok Goyal

Tarun

273,132 görüntüleme • 8 ay önce

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

Dwarkesh Patel

502,162 görüntüleme • 8 ay önce

Were Cursor Wrong to Build Their Own Models: " I don't think so. We need to frame what their aspirations are with these models. There are generalists and there are specialists. Cursor's going to build specialized, coding models that are going to serve specialized coding tasks, especially for enterprise users. They don't need for their models to be good at poetry or teach you how to make an apple pie. Their models are there for professional coders to do professional work That's very powerful and will continue to make the product experience really differentiated." Miles Clements Love to hear your thoughts on this Chamath Palihapitiya Eiso Kant Scott Wu Max Junestrand and the decision of build own or leverage others?

Were Cursor Wrong to Build Their Own Models: " I don't think so. We need to frame what their aspirations are with these models. There are generalists and there are specialists. Cursor's going to build specialized, coding models that are going to serve specialized coding tasks, especially for enterprise users. They don't need for their models to be good at poetry or teach you how to make an apple pie. Their models are there for professional coders to do professional work That's very powerful and will continue to make the product experience really differentiated." Miles Clements Love to hear your thoughts on this Chamath Palihapitiya Eiso Kant Scott Wu Max Junestrand and the decision of build own or leverage others?

Harry Stebbings

25,344 görüntüleme • 4 ay 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.

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.

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