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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

GenLayer

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27,722 次观看 • 4 天前 •via X (Twitter)

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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!

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LM Studio is the most popular way to run open-source LLMs on your own hardware. Your Hermes Agent now runs natively on LM Studio: auto-discovering your models, loading them on demand with the right context size, and using the right reasoning level for each model.

LM Studio is the most popular way to run open-source LLMs on your own hardware. Your Hermes Agent now runs natively on LM Studio: auto-discovering your models, loading them on demand with the right context size, and using the right reasoning level for each model.

Nous Research

185,167 次观看 • 1 个月前

AI CREATORS! Ask to join xMode right now! - it's a tool for elite AI creators. Create your AI Character for free based on your preferences. This will give you 1 photo of your model which you can train a lora on and create endless variations of your model. Watch how simple it is:

AI CREATORS! Ask to join xMode right now! - it's a tool for elite AI creators. Create your AI Character for free based on your preferences. This will give you 1 photo of your model which you can train a lora on and create endless variations of your model. Watch how simple it is:

Roy Granot

14,832 次观看 • 1 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

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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👇

“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👇

girish

447,223 次观看 • 15 天前

The biggest model is no longer the advantage. The right model is. Generic AI can do a little of everything, but it rarely optimizes for what matters in production. Oumi is changing that by making custom models fast and accessible. It feels like we are entering the era of building your own AI. Manos Koukoumidis Oumi

The biggest model is no longer the advantage. The right model is. Generic AI can do a little of everything, but it rarely optimizes for what matters in production. Oumi is changing that by making custom models fast and accessible. It feels like we are entering the era of building your own AI. Manos Koukoumidis Oumi

Leonardo

15,571 次观看 • 2 个月前

#mixtral #mistral #LLM360 Serving Mixtral and LLM360 on FEDML Nexus AI ( We offer Mixtral model endpoints the cheapest in the market: only $0.0005 / 1K tokens! FEDML embraces open source and open model weights. We believe the future of AI belongs to large-scale open collaboration. Today we are excited to support new advances in open-source foundation models: Mixtral, the latest open-source LLM beating Llama2-70B with Mixture-of-Experts (MoE) architecture, and Amber and CrystalCoder backed by LLM360, the framework for open-source LLMs to foster transparency, trust, and collaborative research. Compared to existing fragmented ML products in the market, FEDML Nexus AI is the next-gen cloud service for LLM and Generative AI. It provides an end-to-end platform backed by serverless/decentralized AI infrastructure. Specifically: 1. Economical Serving Engine, ScaleLLM, is where you run your model in cheaper price by optimizing GPU memory and with fully optimized throughput for supporting more concurrent requests. 2. FEDML® Deploy simplifies CLI and MLOps workflow for model deployment on a serverless GPU cloud or on-premise cluster. 3. Serverless Endpoint runs on serverless GPU clouds. With our pay per use policy, we abstract the responsibility of acquiring or leasing an extensive GPU inventory when your are uncertain about your future AI service traffic. The autoscaling feature seamlessly adjusts the backend GPU resources in response to your service traffic. 4. On-premise Deployment helps you own your LLM model on your local environment with AI safety support. 5. FEDML® Launch for serverless GPU clouds. With one-line CLI, it swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, abstracting complex environment setup and management. 6. Zero-code Fine-tuning supported by FEDML® Studio optimizes your model on your domain-specific data without writing any line of source code. 7. Pre-training LLM supports cluster management and experimental tracking. You maintain your training clusters for your urgent needs in your vertical domain. As a closing note, FEDML is gearing up to unveil a cutting-edge service for LLM-based agents and our own cost-effective LLM. Please stay tuned and keep an eye out for upcoming announcements!

#mixtral #mistral #LLM360 Serving Mixtral and LLM360 on FEDML Nexus AI ( We offer Mixtral model endpoints the cheapest in the market: only $0.0005 / 1K tokens! FEDML embraces open source and open model weights. We believe the future of AI belongs to large-scale open collaboration. Today we are excited to support new advances in open-source foundation models: Mixtral, the latest open-source LLM beating Llama2-70B with Mixture-of-Experts (MoE) architecture, and Amber and CrystalCoder backed by LLM360, the framework for open-source LLMs to foster transparency, trust, and collaborative research. Compared to existing fragmented ML products in the market, FEDML Nexus AI is the next-gen cloud service for LLM and Generative AI. It provides an end-to-end platform backed by serverless/decentralized AI infrastructure. Specifically: 1. Economical Serving Engine, ScaleLLM, is where you run your model in cheaper price by optimizing GPU memory and with fully optimized throughput for supporting more concurrent requests. 2. FEDML® Deploy simplifies CLI and MLOps workflow for model deployment on a serverless GPU cloud or on-premise cluster. 3. Serverless Endpoint runs on serverless GPU clouds. With our pay per use policy, we abstract the responsibility of acquiring or leasing an extensive GPU inventory when your are uncertain about your future AI service traffic. The autoscaling feature seamlessly adjusts the backend GPU resources in response to your service traffic. 4. On-premise Deployment helps you own your LLM model on your local environment with AI safety support. 5. FEDML® Launch for serverless GPU clouds. With one-line CLI, it swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, abstracting complex environment setup and management. 6. Zero-code Fine-tuning supported by FEDML® Studio optimizes your model on your domain-specific data without writing any line of source code. 7. Pre-training LLM supports cluster management and experimental tracking. You maintain your training clusters for your urgent needs in your vertical domain. As a closing note, FEDML is gearing up to unveil a cutting-edge service for LLM-based agents and our own cost-effective LLM. Please stay tuned and keep an eye out for upcoming announcements!

TensorOpera AI

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We deployed a fully private AI agent on NuNet in under 5 minutes 🚀 OpenClaw🦞 running Qwen through ollama , one of the hottest open source model families right now, entirely on decentralized compute. No cloud. No API keys. No data leaving the machine. This is what private AI looks like when you actually build it instead of just talking about it. Your model. Your hardware. Your rules. Full walkthrough showing exactly how it works: What should we deploy next?

We deployed a fully private AI agent on NuNet in under 5 minutes 🚀 OpenClaw🦞 running Qwen through ollama , one of the hottest open source model families right now, entirely on decentralized compute. No cloud. No API keys. No data leaving the machine. This is what private AI looks like when you actually build it instead of just talking about it. Your model. Your hardware. Your rules. Full walkthrough showing exactly how it works: What should we deploy next?

NuNet 🌐

87,520 次观看 • 3 个月前

Introducing the 01 Developer Preview. Order or build your own today: The 01 Light is a portable voice interface that controls your home computer. It can see your screen, use your apps, and learn new skills. This is only the beginning for 01— the open-source foundation for this new era of AI devices.

Introducing the 01 Developer Preview. Order or build your own today: The 01 Light is a portable voice interface that controls your home computer. It can see your screen, use your apps, and learn new skills. This is only the beginning for 01— the open-source foundation for this new era of AI devices.

Interpreter

1,408,208 次观看 • 2 年前

Okay it works!!!! Now making the interface for it, I named it [ 👋 Hold a product ] You upload your product photo, you take a photo and it generates one with your AI model holding it Then from there you can press [ Talking video ], write a script and your AI model will present the product for you The AI character is from @lucataco93's tweets 😊

Okay it works!!!! Now making the interface for it, I named it [ 👋 Hold a product ] You upload your product photo, you take a photo and it generates one with your AI model holding it Then from there you can press [ Talking video ], write a script and your AI model will present the product for you The AI character is from @lucataco93's tweets 😊

@levelsio

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Ads built the old internet. AI needs a new model. Dylan Patel says OpenAI’s router could make free AI sustainable, routing everyday queries to small models while reserving agents for high-stakes tasks. “This is how I think OpenAI can finally make money off of the free user.”

Ads built the old internet. AI needs a new model. Dylan Patel says OpenAI’s router could make free AI sustainable, routing everyday queries to small models while reserving agents for high-stakes tasks. “This is how I think OpenAI can finally make money off of the free user.”

a16z

75,465 次观看 • 10 个月前

This approach has made Sonnet the model of choice for developers worldwide. In addition to our new model, we're launching Claude Code, our first coding tool, in a limited research preview. With Claude Code, you can delegate substantial tasks to Claude—right from your terminal.

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1,140,188 次观看 • 1 年前

Sub-agent Model Selection — Different Tasks, Different Models Your main agent runs Qwen3.6-Plus for quality. But not every subtask needs a flagship model. Now sub-agents can use a different model. Create a skill file with model: openai:qwen3.5-plus and the sub-agent runs on that model. Powerful model for the hard parts, fast model for the easy parts. Save tokens without sacrificing quality on what matters.

Sub-agent Model Selection — Different Tasks, Different Models Your main agent runs Qwen3.6-Plus for quality. But not every subtask needs a flagship model. Now sub-agents can use a different model. Create a skill file with model: openai:qwen3.5-plus and the sub-agent runs on that model. Powerful model for the hard parts, fast model for the easy parts. Save tokens without sacrificing quality on what matters.

Qwen

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Meet #DBRX: a general-purpose LLM that sets a new standard for efficient open source models. Use the DBRX model in your RAG apps or use the DBRX design to build your own custom LLMs and improve the quality of your GenAI applications.

Meet #DBRX: a general-purpose LLM that sets a new standard for efficient open source models. Use the DBRX model in your RAG apps or use the DBRX design to build your own custom LLMs and improve the quality of your GenAI applications.

Databricks

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Today, we are releasing Stable Video Diffusion, our first foundation model for generative AI video based on the image model, Stable Diffusion. As part of this research preview, the code, weights, and research paper are now available. Additionally, today you can sign up for our waitlist to access a new upcoming web experience featuring a Text-To-Video interface. To access the model & sign up for our waitlist, visit our website here:

Today, we are releasing Stable Video Diffusion, our first foundation model for generative AI video based on the image model, Stable Diffusion. As part of this research preview, the code, weights, and research paper are now available. Additionally, today you can sign up for our waitlist to access a new upcoming web experience featuring a Text-To-Video interface. To access the model & sign up for our waitlist, visit our website here:

Stability AI

1,024,438 次观看 • 2 年前

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 次观看 • 7 个月前

You can now generate brand-consistent video advertisements for your products on Flair AI 1. Train a model on your brand's aesthetic 2. Train a model on your clothing or product 3. Combine both models in one prompt 4. Animate✨ In beta - comment/RT for access and free credits

mickey friedman

73,897 次观看 • 1 年前

WE, the PEOPLE, do NOT live in a DEMOCRACY, but in a REPUBLIC. A CONSTITUTIONAL REPUBLIC. You can not control everything, but you can control what YOU DO. How are YOU serving YOUR COUNTRY today? What are YOU doing today to make sure we KEEP this REPUBLIC? No matter what role you have in this fight for FREEDOM, remember one thing. NEVER STOP SPEAKING and FIGHTING for the TRUTH and for what is RIGHT. The TIME is NOW. NOTHING CAN STOP WHAT IS COMING.

WE, the PEOPLE, do NOT live in a DEMOCRACY, but in a REPUBLIC. A CONSTITUTIONAL REPUBLIC. You can not control everything, but you can control what YOU DO. How are YOU serving YOUR COUNTRY today? What are YOU doing today to make sure we KEEP this REPUBLIC? No matter what role you have in this fight for FREEDOM, remember one thing. NEVER STOP SPEAKING and FIGHTING for the TRUTH and for what is RIGHT. The TIME is NOW. NOTHING CAN STOP WHAT IS COMING.

The SCIF

36,734 次观看 • 8 个月前

SOMEONE MADE IT SO YOU CAN CODE WITH 174 AI MODELS FROM 23 PROVIDERS FOR FREE one npm package and one install: "npm i -g free-coding-models" switch between any model instantly. benchmark them against each other in real time to see which one actually codes best for your use case. no subscriptions. no API keys. no cost. 174 models. 23 providers, all free, all from your terminal. if you're tired of paying for 3 different AI subscriptions just to compare which model codes better, this is it

SOMEONE MADE IT SO YOU CAN CODE WITH 174 AI MODELS FROM 23 PROVIDERS FOR FREE one npm package and one install: "npm i -g free-coding-models" switch between any model instantly. benchmark them against each other in real time to see which one actually codes best for your use case. no subscriptions. no API keys. no cost. 174 models. 23 providers, all free, all from your terminal. if you're tired of paying for 3 different AI subscriptions just to compare which model codes better, this is it

Om Patel

86,175 次观看 • 2 个月前

How to use 50+ API keys (models) for FREE on OpenClaw API??? - go to - login or register your account - click on "more models" - click on "use case" and select what you need it for - choose the model and open it - click on "view code" → "Generate API key" many models don't allow direct deploy, so use the "view code" button to generate API access basically Nvidia NIM gives you the ability to test almost any model from their list for FREE some of them are not worse than GPT 5.2 or Claude Opus 4.6, some might even perform better depending on the task how to understand if a model is efficient and compare it with others??? - go to - type the model name in search - click on "benchmarks" - you’ll see performance tests and rankings this way you can easily compare free models with paid ones of course there are RPM limits, on many models it’s around ~40 requests per minute each model is different, after generating the API key, RPM limits are shown in the top-right corner nothing stops you from using them, many work perfectly fine, super solid option for first tests and for learning OpenClaw or any other system where you need an AI API model

How to use 50+ API keys (models) for FREE on OpenClaw API??? - go to - login or register your account - click on "more models" - click on "use case" and select what you need it for - choose the model and open it - click on "view code" → "Generate API key" many models don't allow direct deploy, so use the "view code" button to generate API access basically Nvidia NIM gives you the ability to test almost any model from their list for FREE some of them are not worse than GPT 5.2 or Claude Opus 4.6, some might even perform better depending on the task how to understand if a model is efficient and compare it with others??? - go to - type the model name in search - click on "benchmarks" - you’ll see performance tests and rankings this way you can easily compare free models with paid ones of course there are RPM limits, on many models it’s around ~40 requests per minute each model is different, after generating the API key, RPM limits are shown in the top-right corner nothing stops you from using them, many work perfectly fine, super solid option for first tests and for learning OpenClaw or any other system where you need an AI API model

Ronin

58,624 次观看 • 4 个月前