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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...

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

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

Santiago

164,162 Aufrufe • vor 2 Jahren

"One of the biggest misconceptions" Cerebras CFO Bob Komin pushes back on the small-models narrative. "We serve all models, and there is no limit to the size of the models that we can serve. Today, we're serving trillion parameter models. We're serving trillion parameter models that are internal for OpenAI today. We are currently running OpenAI 5.4 and 5.5 with them."
0:50

"One of the biggest misconceptions" Cerebras CFO Bob Komin pushes back on the small-models narrative. "We serve all models, and there is no limit to the size of the models that we can serve. Today, we're serving trillion parameter models. We're serving trillion parameter models that are internal for OpenAI today. We are currently running OpenAI 5.4 and 5.5 with them."

Deirdre Bosa

84,373 Aufrufe • vor 1 Monat

Ollama 0.2 is here! Concurrency is now enabled by default. This unlocks 2 major features: Parallel requests Ollama can now serve multiple requests at the same time, using only a little bit of additional memory for each request. This enables use cases such as: - Handling multiple chat sessions at the same time - Hosting code completion LLMs for your team - Processing different parts of a document simultaneously - Running multiple agents at the same time Run multiple models Ollama now supports loading different models at the same time. This improves several use cases: - Retrieval Augmented Generation (RAG): both the embedding and text completion models can be loaded into memory simultaneously. - Agents: multiple versions of an agent can now run simultaneously - Running large and small models side-by-side Models are automatically loaded and unloaded based on requests and how much GPU memory is available.
0:23

Ollama 0.2 is here! Concurrency is now enabled by default. This unlocks 2 major features: Parallel requests Ollama can now serve multiple requests at the same time, using only a little bit of additional memory for each request. This enables use cases such as: - Handling multiple chat sessions at the same time - Hosting code completion LLMs for your team - Processing different parts of a document simultaneously - Running multiple agents at the same time Run multiple models Ollama now supports loading different models at the same time. This improves several use cases: - Retrieval Augmented Generation (RAG): both the embedding and text completion models can be loaded into memory simultaneously. - Agents: multiple versions of an agent can now run simultaneously - Running large and small models side-by-side Models are automatically loaded and unloaded based on requests and how much GPU memory is available.

ollama

219,394 Aufrufe • vor 2 Jahren

Big news! Now you can use other AI models in Adobe tools. Currently it's Google Imagen 3, GPT-o4 and FLUX along with our Firefly family of models (designed to be commercially safe). You can suggest more models you like to add! I've been advocating for this since day one 🥹🎉
0:50

Big news! Now you can use other AI models in Adobe tools. Currently it's Google Imagen 3, GPT-o4 and FLUX along with our Firefly family of models (designed to be commercially safe). You can suggest more models you like to add! I've been advocating for this since day one 🥹🎉

Kris Kashtanova

23,786 Aufrufe • vor 1 Jahr

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."
1:34

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 Aufrufe • vor 4 Monaten

The future of AI is open-source. And ollama is the easiest way to build AI applications with open-source LLMs. Here's how to build a free, private RAG app using open-source tools. We'll use: - Ollama for LLMs and embedding models - PostgreSQL for data storage and retrieval - pgai Vectorizer for embedding creation and sync (I use Nomic for embeddings and tinnyllama as my LLM but you can substitute them for any models on Ollama)
15:16

The future of AI is open-source. And ollama is the easiest way to build AI applications with open-source LLMs. Here's how to build a free, private RAG app using open-source tools. We'll use: - Ollama for LLMs and embedding models - PostgreSQL for data storage and retrieval - pgai Vectorizer for embedding creation and sync (I use Nomic for embeddings and tinnyllama as my LLM but you can substitute them for any models on Ollama)

Avthar

34,261 Aufrufe • vor 1 Jahr

Demis Hassabis recommendation for college students. He’s still do STEM, math and computer science. Expertise in those fields will help better leverage AI for at least next decade. Those in non-techical majors, really “lean in to” using latest models. AI labs spending so much time creating new models that they’ve only “scratched the surface” of what the models can actually do (huge “capability overhang”). And expertise in any field can be turbocharged by smart use of AI. “Double down on your own agency. The future is still to be written. Don’t listen to anyone that says it’s not.”
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Demis Hassabis recommendation for college students. He’s still do STEM, math and computer science. Expertise in those fields will help better leverage AI for at least next decade. Those in non-techical majors, really “lean in to” using latest models. AI labs spending so much time creating new models that they’ve only “scratched the surface” of what the models can actually do (huge “capability overhang”). And expertise in any field can be turbocharged by smart use of AI. “Double down on your own agency. The future is still to be written. Don’t listen to anyone that says it’s not.”

Bearly AI

217,927 Aufrufe • vor 1 Monat

“Our compute nodes don’t run a full copy of the models we train on IOTA. They actually run a small sliver of the model. This means you can train really large frontier sized models using very small building blocks”. CTO crux unpacks how IOTA ・ SN9’s model parallel architecture allows us to train at scale by splitting our models across multiple machines, and then sewing them together. This is core to SN9’s architecture. See the full Eye On A.I. podcast below.
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“Our compute nodes don’t run a full copy of the models we train on IOTA. They actually run a small sliver of the model. This means you can train really large frontier sized models using very small building blocks”. CTO crux unpacks how IOTA ・ SN9’s model parallel architecture allows us to train at scale by splitting our models across multiple machines, and then sewing them together. This is core to SN9’s architecture. See the full Eye On A.I. podcast below.

IOTA ・ SN9

15,465 Aufrufe • vor 1 Monat

I figured out a way to save thousands of dollars on ClawdBot By using Opus 4.5 for everything, you are SHREDDING your money and tokens In this video I cover how you can use multiple models to get better performance and save $$ Must watch if you use Clawd:
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I figured out a way to save thousands of dollars on ClawdBot By using Opus 4.5 for everything, you are SHREDDING your money and tokens In this video I cover how you can use multiple models to get better performance and save $$ Must watch if you use Clawd:

Alex Finn

158,821 Aufrufe • vor 5 Monaten

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”
1:37

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 Aufrufe • vor 1 Monat

We had some talks about booth models because not everyone seems to understand how they work. Booth models are almost always custom unless they're public uploads. You can't buy the booth models I use, they're custom and not publicly uploaded. Most of the models I use on stream are custom, some are purchased to help promote an avatar creator.
0:32

We had some talks about booth models because not everyone seems to understand how they work. Booth models are almost always custom unless they're public uploads. You can't buy the booth models I use, they're custom and not publicly uploaded. Most of the models I use on stream are custom, some are purchased to help promote an avatar creator.

ChaoticMadness 😺😈 Goddess of Chaos

60,444 Aufrufe • vor 7 Monaten

This is big. The Retrieval Plugin allows ChatGPT to have a memory! The model can now remember information from conversations and store it in the retrieval plugin for later use. This feature is a must if you want to develop GPT-based tools.
0:38

This is big. The Retrieval Plugin allows ChatGPT to have a memory! The model can now remember information from conversations and store it in the retrieval plugin for later use. This feature is a must if you want to develop GPT-based tools.

Lior Alexander

227,586 Aufrufe • vor 3 Jahren

Some of our top customers are still choosing Llama 3.1 8B. For a while, we jumped to whatever hottest, latest model was taking up our twitter feed. 🙈 But as we are quickly realizing, to create a SOTA product, you need a model that fits your exact use case. Here’s what our customers tell us: > a lot of the legwork is actually around prompting > there’s an art to selecting and combining multiple models > benchmarks only show part of the picture. you have to understand the unique quirks of each model. Especially as model releases become more and more frequent, we need a clear way to evaluate new models. We have to break free of the naive trend to migrate to the ‘latest and greatest’. And you can easily achieve this using tools like Cerebras and Braintrust to swap models safely (without breaking production).
1:58

Some of our top customers are still choosing Llama 3.1 8B. For a while, we jumped to whatever hottest, latest model was taking up our twitter feed. 🙈 But as we are quickly realizing, to create a SOTA product, you need a model that fits your exact use case. Here’s what our customers tell us: > a lot of the legwork is actually around prompting > there’s an art to selecting and combining multiple models > benchmarks only show part of the picture. you have to understand the unique quirks of each model. Especially as model releases become more and more frequent, we need a clear way to evaluate new models. We have to break free of the naive trend to migrate to the ‘latest and greatest’. And you can easily achieve this using tools like Cerebras and Braintrust to swap models safely (without breaking production).

Cerebras

346,446 Aufrufe • vor 6 Monaten

They did use 3D models and we can fly through the scenes and check it all out! #threejs Shopify Using the Needle three.js inspector
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They did use 3D models and we can fly through the scenes and check it all out! #threejs Shopify Using the Needle three.js inspector

🌵 Marcel Wiessler

157,284 Aufrufe • vor 6 Monaten

With the launch of GLM 5.2 this week, I see everyone asking "have open models caught up to closed models?" The more interesting question that's getting missed: what can you do with an open model that you can't do with a closed one? You can specialize them. And when you do, the number of economically valuable tasks open models can do actually subsumes that of closed models. Charlie O'Neill explaining this:
1:30

With the launch of GLM 5.2 this week, I see everyone asking "have open models caught up to closed models?" The more interesting question that's getting missed: what can you do with an open model that you can't do with a closed one? You can specialize them. And when you do, the number of economically valuable tasks open models can do actually subsumes that of closed models. Charlie O'Neill explaining this:

Madison Kanna

16,373 Aufrufe • vor 13 Tagen

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially Ethan BdM , Zhengdong , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.
3:20

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially Ethan BdM , Zhengdong , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.

Andy Hall

33,696 Aufrufe • vor 3 Monaten

1/ In GitHub Copilot CLI, you can move across models depending on the job. Use faster models to scaffold and explore quickly. Use deeper reasoning models to plan, understand requirements, and make the right tradeoffs. Then GPT-5.5 can take that plan and turn it into working code, iterating, resolving errors, using tools, and validating results along the way.
1:18

1/ In GitHub Copilot CLI, you can move across models depending on the job. Use faster models to scaffold and explore quickly. Use deeper reasoning models to plan, understand requirements, and make the right tradeoffs. Then GPT-5.5 can take that plan and turn it into working code, iterating, resolving errors, using tools, and validating results along the way.

Satya Nadella

54,754 Aufrufe • vor 2 Monaten

Alphabet CEO Sundar Pichai: "I would love to do a side-by-side comparison of Microsoft's own models and our models, any day, any time they're using, they're using someone else's models" $GOOG $GOOGL
1:35

Alphabet CEO Sundar Pichai: "I would love to do a side-by-side comparison of Microsoft's own models and our models, any day, any time they're using, they're using someone else's models" $GOOG $GOOGL

The Transcript

73,964 Aufrufe • vor 1 Jahr

We are opening up the beta of our new AI writing tool to Exclusive Models. If you're exclusive to JFF, try it out and let us know your thoughts. At our Anniversary Conference, lots of models said they needed help making their posts more creative. This is a tool to help models take what they write and help them make better posts. Does this replace a human? No! In fact, if you are an erotic writer, apply to be in our Business Directory so models can hire you to help them out!
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Sensitive content

We are opening up the beta of our new AI writing tool to Exclusive Models. If you're exclusive to JFF, try it out and let us know your thoughts. At our Anniversary Conference, lots of models said they needed help making their posts more creative. This is a tool to help models take what they write and help them make better posts. Does this replace a human? No! In fact, if you are an erotic writer, apply to be in our Business Directory so models can hire you to help them out!

JustForFans

15,325 Aufrufe • vor 3 Jahren