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🚀 Amazon Nova Premier, our most capable teacher model for creating custom distilled models, is now available on Amazon Bedrock! Built for complex tasks like Retrieval-Augmented Generation (RAG), function calling, and agentic coding, its one-million-token context window enables analysis of large datasets while being the most cost-effective proprietary model... show more

Amazon Science

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Meet Amazon Nova 2 reasoning models Nova has introduced its next generation of foundation models that deliver frontier intelligence with industry-leading price performance 1/ Amazon Nova 2 Lite, is a fast, cost-effective model designed for everyday AI tasks. 2/ Amazon Nova 2 Pro (preview), Nova’s most intelligent model built for complex AI workloads. These models offer developers control to manage thinking budgets, native support for agentic workflows, and provide a one-million-token context window for richer interactions.

Meet Amazon Nova 2 reasoning models Nova has introduced its next generation of foundation models that deliver frontier intelligence with industry-leading price performance 1/ Amazon Nova 2 Lite, is a fast, cost-effective model designed for everyday AI tasks. 2/ Amazon Nova 2 Pro (preview), Nova’s most intelligent model built for complex AI workloads. These models offer developers control to manage thinking budgets, native support for agentic workflows, and provide a one-million-token context window for richer interactions.

Amazon Web Services

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🚨 Big news for AI innovation: Claude Opus 4 and Claude Sonnet 4, Anthropic's most advanced models, are now available in Amazon Bedrock. These powerful models offer hybrid reasoning, 200K token context windows, and are designed for AI agents. From financial analysis to high-quality writing, to enhanced reasoning, coding, agentic capabilities and more—all with the enterprise-grade security of Amazon Web Services.

🚨 Big news for AI innovation: Claude Opus 4 and Claude Sonnet 4, Anthropic's most advanced models, are now available in Amazon Bedrock. These powerful models offer hybrid reasoning, 200K token context windows, and are designed for AI agents. From financial analysis to high-quality writing, to enhanced reasoning, coding, agentic capabilities and more—all with the enterprise-grade security of Amazon Web Services.

Amazon

123,056 Aufrufe • vor 1 Jahr

The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:

The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:

NVIDIA AI

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Hy3, the new 295B MoE model from Tencent Hy, is now free in Nous Portal for the next two weeks! It is focused on cost-effective agentic use, and particularly strong on coding, tool-calling reliability, reasoning, and 256K long-context tasks.

Hy3, the new 295B MoE model from Tencent Hy, is now free in Nous Portal for the next two weeks! It is focused on cost-effective agentic use, and particularly strong on coding, tool-calling reliability, reasoning, and 256K long-context tasks.

Nous Research

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Meet Palmyra X5—the AI that thinks big, can digest entire books in seconds, and is now available in Amazon Bedrock. This powerhouse model can: 📚 Process huge amounts of data 🤯Understand context like never before 🚀Unlock new possibilities for AI apps Here’s a whiteboard breakdown 👇

Meet Palmyra X5—the AI that thinks big, can digest entire books in seconds, and is now available in Amazon Bedrock. This powerhouse model can: 📚 Process huge amounts of data 🤯Understand context like never before 🚀Unlock new possibilities for AI apps Here’s a whiteboard breakdown 👇

Amazon

68,400 Aufrufe • vor 1 Jahr

MiniMax M2.1 is officially live🚀 Built for real-world coding and AI-native organizations — from vibe builds to serious workflows. A SOTA 10B-activated OSS coding & agent model, scoring 72.5% on SWE-multilingual and 88.6% on our newly open-sourced VIBE-bench, exceeding leading closed-source models like Gemini 3 Pro and Claude 4.5 Sonnet. The most powerful OSS model for the agentic era is here.

MiniMax M2.1 is officially live🚀 Built for real-world coding and AI-native organizations — from vibe builds to serious workflows. A SOTA 10B-activated OSS coding & agent model, scoring 72.5% on SWE-multilingual and 88.6% on our newly open-sourced VIBE-bench, exceeding leading closed-source models like Gemini 3 Pro and Claude 4.5 Sonnet. The most powerful OSS model for the agentic era is here.

MiniMax (official)

1,074,412 Aufrufe • vor 8 Monaten

Meet Apodex 1.1: Scaling Agentic Intelligence for Complex Work Open Source Harness: Open Weights: We’re excited to introduce Apodex 1.1, our new model family built to scale agentic intelligence for professional work. 🧠 Frontier-level intelligence for complex work Apodex 1.1 brings frontier-level agentic performance across complex professional work, scientific research, financial analysis, and deep search. 🤝 Asynchronous Agent Team Apodex 1.1 can break down complex tasks, coordinate multiple agents in parallel, continuously integrate their findings, and let you step in to guide or redirect the work at any time. 🔬 Open-source research workbench We’re open-sourcing FrontierAgent—a locally deployable research workbench for the Apodex 1.1 family, including asynchronous Agent Team. Available now: 🔹 Apodex 1.1 — our most capable frontier model, available through the Apodex online workbench 🔹 Apodex 1.1 mini — open-weight model for running complex work locally 🔹 FrontierAgent — open-source, locally deployable research workbench 🌐 Live workbench: 🦾 API platform: 📃 Paper:

Meet Apodex 1.1: Scaling Agentic Intelligence for Complex Work Open Source Harness: Open Weights: We’re excited to introduce Apodex 1.1, our new model family built to scale agentic intelligence for professional work. 🧠 Frontier-level intelligence for complex work Apodex 1.1 brings frontier-level agentic performance across complex professional work, scientific research, financial analysis, and deep search. 🤝 Asynchronous Agent Team Apodex 1.1 can break down complex tasks, coordinate multiple agents in parallel, continuously integrate their findings, and let you step in to guide or redirect the work at any time. 🔬 Open-source research workbench We’re open-sourcing FrontierAgent—a locally deployable research workbench for the Apodex 1.1 family, including asynchronous Agent Team. Available now: 🔹 Apodex 1.1 — our most capable frontier model, available through the Apodex online workbench 🔹 Apodex 1.1 mini — open-weight model for running complex work locally 🔹 FrontierAgent — open-source, locally deployable research workbench 🌐 Live workbench: 🦾 API platform: 📃 Paper:

Apodex

922,113 Aufrufe • vor 17 Tagen

HERMES AGENT SUPPORTS 300+ MODELS. PICKING THE RIGHT ONE PER TASK IS THE DIFFERENCE BETWEEN $5/MONTH AND $50. STARTING OUT: Claude Sonnet 4.6. official recommendation from Nous Research. "the model this project was built and tested with." strong reasoning. reliable tool calling. mid-range pricing. PREMIUM TIER: Claude Opus 4.8. best coding benchmarks available. self-correcting reasoning. catches its own mistakes. 1M context. use for demanding tasks where quality matters. GPT-5.5. #1 Chatbot Arena. #1 GPQA Diamond reasoning (94.1%). #1 creative writing. 2M context. handles entire codebases in one pass. Grok 4.30. the only frontier model with live X firehose access. real-time social data, breaking news, market sentiment. connects via Grok OAuth. no separate API key. Grok-Composer-2.5-Fast (v0.17.0). Cursor's coding model. 200K context. available through your Grok subscription via OAuth. no extra cost if you already pay for Grok. MID-RANGE TIER: Claude Sonnet 4.6. best balance of quality and cost for daily use. strongest prose and tool calling in this tier. Gemini 2.5 Pro. Google Search grounding built in. cites sources. verifies claims. pulls current data. 2M context. best for research-heavy workflows. GPT-4.1. reliable tool calling. solid general reasoning. good middle ground when you need OpenAI compatibility. BUDGET TIER: Claude Haiku 4.5. fastest Anthropic model. cheapest paid Claude option. strong at classification, routing, simple queries. use for auxiliary tasks: compression, vision, web extraction, approval scoring. DeepSeek V4. best cost-to-quality ratio in the market. 90% cache discount on repeated context. use for sub-agents and bulk parallel work. DeepSeek V4 Flash. cheapest paid model worth using. 1M context. MIT license. self-hostable. use for cron jobs, monitoring, routine searches. MiniMax M3. Nous Research and MiniMax collaborating on optimization. 1M context via lightning attention. 59% SWE-Bench Pro. beats several premium models on coding. one of the most-used models inside Hermes. FREE / LOCAL: Qwen 3.5 27B via Ollama. 16GB VRAM. reliable tool calling. best free local model for Hermes as of mid-2026. Qwen 3 8B. 8GB VRAM. fits a $7 VPS. handles routine tasks at zero API cost. Llama 4 Maverick. best open-weight tool calling. 1M context. needs more VRAM but strongest local option. HOW TO ASSIGN MODELS: main model: Desktop app / Dashboard → Models → switch sub-agent model: set in Desktop app, Dashboard, or config.yaml: delegation: model: "deepseek/deepseek-v4" auxiliary models (compression, vision, web extract): Desktop app / Dashboard → Models → Auxiliary Haiku 4.5 or Gemini Flash work well here. saves significantly when your main model is premium. per-profile: each Hermes profile gets its own model. Scout on DeepSeek. Analyst on Sonnet. Briefer on budget model. Coder on Opus. per-cron-job: pin a specific model to any cron job. morning brief on Haiku. deep research on Sonnet. monitoring on DeepSeek Flash. each job uses only the model it needs. per-session: /model deepseek/deepseek-v4-flash hot-swap mid-conversation. no restart needed. FALLBACK CHAINS: if your primary model is unavailable, Hermes automatically switches to the next provider. rate limit or server error = next model in the chain. no failed runs. no manual intervention. set in Desktop app, Dashboard, or config.yaml: fallback_providers: - openrouter - nous - codex PROVIDER PATHS: OPENROUTER: 300+ models under one API key. pay per token. most flexible. NOUS PORTAL: 300+ models + Tool Gateway (web search, image gen, TTS, browser). one OAuth. one subscription. 10% off token-billed providers. CHATGPT SUB: GPT-5.5 + Grok via OAuth. included tokens with $20 subscription. OLLAMA: free. local. private. zero API cost. your hardware only. mix providers across profiles and tasks. Scout on OpenRouter. Analyst on Nous Portal. Coder on ChatGPT sub. Monitor on Ollama. THE RULE: premium for work that needs deep reasoning. mid-range for daily driver tasks. budget for volume and background work. free for monitoring and routine jobs. pricing changes fast. check openrouter ai for current rates before committing. Which is your favourite model and for what task? full 15 levels breakdown in the article 👇

YanXbt

17,138 Aufrufe • vor 2 Monaten

Verba is an open source Retrieval Augmented Generation (RAG) application that performs RAG on your own data. To showcase its capabilities, we've customized it as an Airbnb chatbot using Airbnb’s customer documentation. How it works: • Ask any questions, related to your booking, policies, or anything related to your Airbnb experience. • Get relevant, human-like responses: Verba provides natural and informative answers. • Access original sources: One of the standout features of RAG is its ability to directly indicate the sources it used to generate each response. Under the hood, Verba uses a RAG pipeline to deliver these exceptional results. Your query is transformed into a numerical representation (vector) and be used to search through our vector database for the most similar context using Hybrid Search. The most relevant context is then combined with your original question and fed into a powerful large language model (LLM). The LLM will then use all of that information to generate a conversational response. Et voilà! 💫 Try Verba: Verba on GitHub: Learn more in our video:

Verba is an open source Retrieval Augmented Generation (RAG) application that performs RAG on your own data. To showcase its capabilities, we've customized it as an Airbnb chatbot using Airbnb’s customer documentation. How it works: • Ask any questions, related to your booking, policies, or anything related to your Airbnb experience. • Get relevant, human-like responses: Verba provides natural and informative answers. • Access original sources: One of the standout features of RAG is its ability to directly indicate the sources it used to generate each response. Under the hood, Verba uses a RAG pipeline to deliver these exceptional results. Your query is transformed into a numerical representation (vector) and be used to search through our vector database for the most similar context using Hybrid Search. The most relevant context is then combined with your original question and fed into a powerful large language model (LLM). The LLM will then use all of that information to generate a conversational response. Et voilà! 💫 Try Verba: Verba on GitHub: Learn more in our video:

Femke Plantinga

120,565 Aufrufe • vor 2 Jahren

Today we're introducing Gemini 3.7 Flash, our most intelligent workhorse model yet for coding and agents. This model brings substantial gains across software engineering, web development, and complex knowledge work. Now through the end of the year, Gemini 3.7 Flash is available at an introductory price* of $0.75/1M input and $3.75/1M output tokens — making it easier and more cost-effective for developers and customers to scale production-ready agents.

Today we're introducing Gemini 3.7 Flash, our most intelligent workhorse model yet for coding and agents. This model brings substantial gains across software engineering, web development, and complex knowledge work. Now through the end of the year, Gemini 3.7 Flash is available at an introductory price* of $0.75/1M input and $3.75/1M output tokens — making it easier and more cost-effective for developers and customers to scale production-ready agents.

Google

1,178,368 Aufrufe • vor 28 Tagen

Excited about the launch of Amazon Nova Sonic, our new speech-to-speech model that helps make AI voice applications feel remarkably natural. It's designed to understand not just what people say, but how they say it – working with tone, style, and conversation flow including pauses and interruptions. Nova Sonic delivers speech understanding and generation through a single, unified model, making it easier for builders to develop voice applications that maintain important context and nuance for customer service, AI agents, and other use cases across industries. It’s available in Amazon Bedrock now. Look forward to seeing what teams build with Nova Sonic!

Excited about the launch of Amazon Nova Sonic, our new speech-to-speech model that helps make AI voice applications feel remarkably natural. It's designed to understand not just what people say, but how they say it – working with tone, style, and conversation flow including pauses and interruptions. Nova Sonic delivers speech understanding and generation through a single, unified model, making it easier for builders to develop voice applications that maintain important context and nuance for customer service, AI agents, and other use cases across industries. It’s available in Amazon Bedrock now. Look forward to seeing what teams build with Nova Sonic!

Andy Jassy

155,882 Aufrufe • vor 1 Jahr

Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

Kaustubh Sridhar

52,158 Aufrufe • vor 1 Jahr

“AI MODELS COULD BECOME 10× SMALLER WITHOUT LOSING MOST OF THEIR INTELLIGENCE.” Mark Zuckerberg says one of the most powerful developments in AI is model distillation: taking the capabilities of a much larger model and transferring them into a much smaller one. - Meta's huge frontier models can serve as "teachers" for smaller models. - Zuckerberg says a model could potentially retain around 90–95% of the larger model's intelligence while being only ~10% of its size. - That means dramatically lower inference costs, memory requirements and computing power. - Developers can distill and customize these capabilities for their own applications. And that's where open-source models come into the picture.

“AI MODELS COULD BECOME 10× SMALLER WITHOUT LOSING MOST OF THEIR INTELLIGENCE.” Mark Zuckerberg says one of the most powerful developments in AI is model distillation: taking the capabilities of a much larger model and transferring them into a much smaller one. - Meta's huge frontier models can serve as "teachers" for smaller models. - Zuckerberg says a model could potentially retain around 90–95% of the larger model's intelligence while being only ~10% of its size. - That means dramatically lower inference costs, memory requirements and computing power. - Developers can distill and customize these capabilities for their own applications. And that's where open-source models come into the picture.

SciTech Era

77,374 Aufrufe • vor 1 Monat

Try Google Gemini 3 Pro now on Verdent! Enjoy up to 85% OFF usage cost to build more. - 70% off for 30 days: Gemini 3 Pro usage cost in Verdent is 70% OFF. - 2x credits on Verdent subscription: With our limited-time double credits bonus, Gemini 3 Pro costs is 85% OFF of the official price. - Gemini 3 Pro keeps full-project context for real development tasks. Its agentic execution ensures results are correct and shippable. Come build with Google's latest powerful model for coding! Enjoy the Black Friday Sales now.

Try Google Gemini 3 Pro now on Verdent! Enjoy up to 85% OFF usage cost to build more. - 70% off for 30 days: Gemini 3 Pro usage cost in Verdent is 70% OFF. - 2x credits on Verdent subscription: With our limited-time double credits bonus, Gemini 3 Pro costs is 85% OFF of the official price. - Gemini 3 Pro keeps full-project context for real development tasks. Its agentic execution ensures results are correct and shippable. Come build with Google's latest powerful model for coding! Enjoy the Black Friday Sales now.

Verdent

154,352 Aufrufe • vor 9 Monaten

🌌 GPT-6 Astra from OpenAI Developers is now generally available in GitHub Copilot. This new model is designed for long-horizon, autonomous coding and agentic tasks. Our internal testing shows it plans and validates as it goes, batches diagnosis with verification, and independently confirms its results before declaring a task done. That translated into stronger performance on extensive coding jobs with fewer steps than prior OpenAI models. Try it out in the GitHub Copilot app, CLI, or Visual Studio Code. 🚀

🌌 GPT-6 Astra from OpenAI Developers is now generally available in GitHub Copilot. This new model is designed for long-horizon, autonomous coding and agentic tasks. Our internal testing shows it plans and validates as it goes, batches diagnosis with verification, and independently confirms its results before declaring a task done. That translated into stronger performance on extensive coding jobs with fewer steps than prior OpenAI models. Try it out in the GitHub Copilot app, CLI, or Visual Studio Code. 🚀

GitHub

169,907 Aufrufe • vor 6 Tagen

Microsoft ran its graph system against vector RAG on 8k, 120k, and a full million token context window, and the million token giant lost Jonathan Larson from Microsoft Research put up the numbers → LazyGraphRAG won 92, 90, and 91 percent of data-local questions, the exact place plain RAG was supposed to be strong → and did it at a tenth of the cost of the million token run the lesson buried in the benchmark → a bigger context window is not memory, structured memory is throwing more tokens at the problem was just the expensive way to be wrong open source on GitHub, the lesson a $1500 course would charge you for

Microsoft ran its graph system against vector RAG on 8k, 120k, and a full million token context window, and the million token giant lost Jonathan Larson from Microsoft Research put up the numbers → LazyGraphRAG won 92, 90, and 91 percent of data-local questions, the exact place plain RAG was supposed to be strong → and did it at a tenth of the cost of the million token run the lesson buried in the benchmark → a bigger context window is not memory, structured memory is throwing more tokens at the problem was just the expensive way to be wrong open source on GitHub, the lesson a $1500 course would charge you for

NO1ennn

29,189 Aufrufe • vor 1 Monat

$Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.$

Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.

Milk Road AI

27,908 Aufrufe • vor 2 Monaten

New short course on sophisticated RAG (Retrieval Augmented Generation) techniques is out! Taught by Jerry Liu and Anupam Datta of LlamaIndex 🦙 and TruEra , this teaches advanced techniques that help your LLM generate good answers. Topics include: - Sentence-window retrieval, which retrieves not just the most relevant sentence, but a window of sentences around it for higher quality context. - Auto-merging retrieval, which organizes your document into a hierarchical tree structure, where each parent node's text is split among its child nodes. Based on the relevance of the child nodes to a user query, this lets you better decide whether the entire parent node should be provided as context to the LLM. - Evaluation methodology for separately evaluating the quality of the key steps of RAG (context relevance, answer relevance, groundedness) so that you can perform error analysis, identify which part of your pipeline needs work, and tune components systematically. Please check out the course!

New short course on sophisticated RAG (Retrieval Augmented Generation) techniques is out! Taught by Jerry Liu and Anupam Datta of LlamaIndex 🦙 and TruEra , this teaches advanced techniques that help your LLM generate good answers. Topics include: - Sentence-window retrieval, which retrieves not just the most relevant sentence, but a window of sentences around it for higher quality context. - Auto-merging retrieval, which organizes your document into a hierarchical tree structure, where each parent node's text is split among its child nodes. Based on the relevance of the child nodes to a user query, this lets you better decide whether the entire parent node should be provided as context to the LLM. - Evaluation methodology for separately evaluating the quality of the key steps of RAG (context relevance, answer relevance, groundedness) so that you can perform error analysis, identify which part of your pipeline needs work, and tune components systematically. Please check out the course!

Andrew Ng

655,661 Aufrufe • vor 2 Jahren

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

With Kimi K3 Day-0 on vLLM: Open Frontier Intelligence for Everyone 🚀 At 2.8 trillion parameters, Moonshot AI's Kimi K3 is one of the most powerful open-weight models ever released. Starting today, you can serve it on vLLM the moment the weights are public. What K3 brings: 🧠 2.8T-parameter Mixture-of-Experts (16 of 896 experts active per token) 📚 1M-token context window 🛠️ Native multimodal understanding, including vision ⚡ Kimi Delta Attention: a hybrid of linear and full attention that makes million-token context affordable Huge thank you to Kimi.ai AI for the model release and partnership, Inferact for leading the vLLM optimizations, and to our partners at NVIDIA, AMD, and the broader vLLM community. 1/6

vLLM

103,813 Aufrufe • vor 1 Monat

🚀 Meta FAIR is releasing several new research artifacts on our road to advanced machine intelligence (AMI). These latest advancements are transforming our understanding of perception. 1️⃣ Meta Perception Encoder: A large-scale vision encoder that excels across several image & video tasks. 2️⃣ Meta Perception Language Model: A fully open & reproducible vision-language model designed to tackle visual recognition tasks. 3️⃣ Meta Locate 3D: An end-to-end model for accurate object localization in 3D environments. 4️⃣ Releasing model weights for our 8B-parameter Dynamic Byte Latent Transformer, an alternative to traditional tokenization methods with the potential to redefine the standards for language model efficiency and reliability. 5️⃣Collaborative Reasoner: A framework for evaluating & improving collaborative reasoning skills in language models. Download the code, datasets, and research papers and learn more about how these artifacts are paving the way for more efficient and accurate AI systems.➡️

🚀 Meta FAIR is releasing several new research artifacts on our road to advanced machine intelligence (AMI). These latest advancements are transforming our understanding of perception. 1️⃣ Meta Perception Encoder: A large-scale vision encoder that excels across several image & video tasks. 2️⃣ Meta Perception Language Model: A fully open & reproducible vision-language model designed to tackle visual recognition tasks. 3️⃣ Meta Locate 3D: An end-to-end model for accurate object localization in 3D environments. 4️⃣ Releasing model weights for our 8B-parameter Dynamic Byte Latent Transformer, an alternative to traditional tokenization methods with the potential to redefine the standards for language model efficiency and reliability. 5️⃣Collaborative Reasoner: A framework for evaluating & improving collaborative reasoning skills in language models. Download the code, datasets, and research papers and learn more about how these artifacts are paving the way for more efficient and accurate AI systems.➡️

AI at Meta

163,374 Aufrufe • vor 1 Jahr