正在加载视频...

视频加载失败

Happy 2025! Introducing LM Studio 0.3.6 🚀 - New vision models: Qwen2VL and QVQ (both GGUF + MLX) 🤩 - Function Calling API (in beta) 🧰 - New installer on Windows: choose drive (finally 😮‍💨) - In-app updates are much smaller & have a progress bar! 🟩🟩⬜️⬜️ - Update...

30,165 次观看 • 1 年前 •via X (Twitter)

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

i just ran Google's brand new Unsloth Gemma4 12B dense GGUF on my RTX 4060 using llama.cpp + CUDA 13.2 21 tokens per second. on a budget consumer GPU. locally. no API. no cloud. no subscription. and the benchmarks are absolutely cooked # first let's talk architecture because this is genuinely different every multimodal model you've used has a frozen vision encoder + frozen audio encoder + LLM backbone glued together Gemma 4 12B is different it's a single decoder only transformer. that's it. vision? raw 48×48 pixel patches → one matmul → projected directly into the LLM audio? raw 16kHz signal sliced into 40ms frames → linear projection → same LLM input space no encoder tax. no latency penalty. no fragmented memory to put the encoder savings in perspective: old Gemma 4 26B approach: - 550M param vision encoder (frozen) - 300M param audio encoder (frozen) - LLM backbone Gemma 4 12B: - 35M param vision embedder (a single matmul) - no audio encoder at all - LLM backbone handles EVERYTHING 550M → 35M for vision alone. that's a 15x reduction this is why the gemma-4-12b-it-Q4_K_M.gguf is just 6.6 GBs!!! and it has 256K native context context # Benchmarks: AIME 2026 (math olympiad): 77.5% GPQA Diamond (expert science): 78.8% LiveCodeBench v6 (real code): 72% Codeforces ELO: 1659 MMLU Pro: 77.2% MATH-Vision: 79.7% BigBench Extra Hard: 53% inference → llama.cpp, LM Studio, vLLM, SGLang llamacpp flags: -m "gemma-4-12b-it-Q4_K_M.gguf" -ngl 99 -c 8000 -v --port 8080 Available on huggingface now! Link below

Alok

279,768 次观看 • 1 个月前

We are excited to unveil the latest version of the AIOZ Node: The Version 4.0 update! This update includes a new user interface and brings substantial functional improvements, enhancing your overall experience for increased productivity and efficiency. More information below: The standout feature of AIOZ Node v4.0 is the introduction of the Transcoding functionality, which is currently available in beta. This functionality enables your node to participate in video transcoding, which converts video files into different formats for various digital devices and media platforms. By enabling transcoding, your node can contribute more significantly to the AIOZ Network, expanding the network's capabilities and potential $AIOZ token rewards. While the transcoding functionality is currently in beta, the upcoming AIOZ W3Stream integration, a DePIN Video Infrastructure due for release in Q3 2024, will unlock the full potential of your node and enable seamless video transcoding tasks. To get started with AIOZ Node v4.0, you simply need to visit our official website to download the latest version of the AIOZ Node: This download process is very straightforward, and with a one-click installation process, you can set up AIOZ Node v4.0 to start running on your device within a few minutes. If you are already running an AIOZ Node on your device, the version 4.0 update will be applied automatically, ensuring you have the latest features and improvements without hassle! With the Node v4.0 update running on your device, you can proceed to familiarize yourself with the new layout, check out the performance improvements, and start transcoding to see how it enhances your contributions to the network! Learn More: $AIOZ

AIOZ Network

20,428 次观看 • 2 年前

HERMES AGENT NOW SUPPORTS COMPUTER USE ON WINDOWS AND LINUX. CLICKS, TYPES, SCROLLS YOUR DESKTOP IN THE BACKGROUND WHILE YOU WORK. computer use was macOS only. now it works on Windows and Linux too via Cua. Nous Research HOW IT WORKS: cua-driver runs as an MCP server. Hermes takes a screenshot with numbered elements. clicks element #14 (the search field). types a query. submits. reads the result. during all of this: → your cursor stays where you left it → keyboard focus doesn't change → windows don't come to front → macOS doesn't switch Spaces you and the agent co-work on the same machine. WHAT IT CAN DO: → find your latest Stripe email and summarize it → fill forms in a web app that has no API → navigate desktop apps (Mail, browser, Finder) → interact with any GUI application → extract data from apps only accessible via screen WORKS WITH ANY VISION MODEL: not locked to Anthropic. | Provider | Works | |---|---| | Claude (Sonnet/Opus) | best overall | | GPT-4+, GPT-5.5 | full support | | Gemini (via OpenRouter) | full support | | Local vLLM / LM Studio | if model supports vision | | Text-only models | degraded (accessibility tree only) | SETUP: hermes computer-use install or: hermes tools → Computer Use → cua-driver grant permissions when prompted: → Accessibility (system settings) → Screen Recording (system settings) start a session: hermes -t computer_use chat or add to config.yaml / Desktop app settings to enable permanently. SAFETY: → destructive actions require your approval → blocked key combos: empty trash, force delete, lock screen, log out → blocked type patterns: curl | bash, sudo rm -rf /, fork bombs → agent cannot click permission dialogs → agent cannot type passwords → agent cannot follow instructions embedded in screenshots pair with approvals.mode: manual if you want every single click confirmed. TOKEN NOTE: screenshots are expensive. each one adds vision tokens to context. use computer_use for tasks where no API exists. if the tool has an API or MCP server, use that instead. 15 levels of Hermes Agent👇

YanXbt

29,127 次观看 • 1 个月前

Full Fine-tuning vs. Freezing Layers. Interact 👉 and == Full Fine-tuning == A real network has many — three layers in this example, billions of parameters in a production model. What does fine-tuning look like when you update all of them? That’s full fine-tuning: continue training every weight in the pretrained network on your new task. Every layer’s W gets its own ΔW. Nothing is frozen — every parameter is in play. Think of an MLP as a chain of prerequisites leading to an advanced course. Layer 1 might be Linear Algebra, layer 2 Probability, layer 3 Advanced Machine Learning — each one building on what came before. Fine-tuning is what happens during graduate study: the foundations are already there from undergrad, so you’re not re-learning. Full fine-tuning is reviewing every prerequisite to see what new topics have appeared and what discoveries the field has made since the last time you sat through them. Effective — but exhausting. This diagram shows the same three-layer MLP twice, side by side. On the left, the pretrained network runs on input X: three weight matrices W₁, W₂, W₃, each followed by a ReLU activation. Full fine-tuning gives the model the most freedom to specialize. Every parameter can move — and every parameter that can move must be stored. But not every prerequisite needs revisiting. The further you go back in the chain, the less the material has changed since pretraining — the linear-algebra basics under your computer-vision course are largely the same as they ever were. The next page does exactly that: freeze the prerequisites that haven’t moved, and only refresh the advanced one closest to your specialization. == Freezing Layers == Full fine-tuning reviewed every prerequisite — Linear Algebra, Probability, Advanced ML — to refresh each subject with the latest topics. Effective, but exhausting. Then you realize something. The prerequisites haven’t actually changed that much. Linear Algebra is still Linear Algebra; the matrix decompositions you learned still hold. Probability is still Probability; the distributions and Bayes’ rule haven’t moved. Almost all the new material — the new ideas, the recent discoveries — lives in the advanced layer at the top. That’s freezing layers: keep the prerequisite layers fixed at their pretrained state, and only update the advanced one. In the diagram below, W1​ and W2​ — the foundational prerequisites — stay frozen. Only W3​ — the layer closest to your task-specific output — gets a ΔW.

Tom Yeh

27,587 次观看 • 3 个月前

🪴 GT Protocol Monthly Recap: May 2026 May focused on launching advanced trading infrastructure, introducing AI risk-management tools, and shipping major platform upgrades. 🚀 Hyperliquid Vaults Live Run multiple algorithmic strategies on a single Hyperliquid Vault inside GT App. Enjoy automated execution, auto-rebalancing, and protocol-level security. You can find Vault trading on the Hyperliquid exchange account connection page in the Trade on Vault section. Try it in GT App 👉 🤖 AI Hedge Fund Experiment Live An experimental AI Hedge Fund powered by 5 independent LLM models is live on Hyperliquid. Each model manages $10,000 to test different AI trading personalities and allocation strategies. Discover it now here 👉 📈 Isolated Margin & AI Risk Tools Isolated Margin is live across GT App for precise risk management. Enhanced with AI-powered logic, it assists with dynamic asset monitoring and smarter strategy deployment. Try it in GT App 👉 🔥 Top Strategy Performance Top trader strategies like "lebakien" achieved over +141% profit this month. Users can explore metrics and follow the strategies of top traders directly in the marketplace. Explore Marketplace 👉 🛠 Key Product Updates ⚙️ Strategy Discovery: enhanced demo trading flows and top trader strategy integration. ⚙️ AI Strategy Chat: demoed a flow to create, launch, and test strategies via natural language chat. ⚙️ Advanced Execution: added manual safety orders for granular control over active positions. ⚙️ Testing & Validation: optimized historical data validation for more accurate strategy testing. ⚙️ Knowledge Hub: launched GT Protocol Learn and a new Knowledge Base for streamlined support. ⚙️ Performance: upgraded website structure and improved overall page responsiveness. Find all the latest GT App updates Here 👉 Discover guides, insights, and resources in Learn 👉 and Knowledge Base 👉 📰 GT Protocol AI Digests 4 new AI Digest issues (No.89–92) are live on Medium, covering AI-native hardware, data privacy, and the evolution of AI agents. Read More 👉 May brought institutional-grade AI strategy management closer to every user.

GT Protocol

32,774 次观看 • 1 个月前

A viral paper "Language Model Represents Space and Time" recently claims that LLMs learn "world models". As much as I like Max Tegmark's works, I disagree with their definition of world model. World model is a core concept in AI agent and decision making. It is our mental simulation of how the world works given interventions (or lack thereof). A world model captures causality and intuitive physics, telling the agent what is likely and what is impossible. It can and should be used for counterfactual reasoning, i.e. "what ifs": what would happen if I knock over a cup of water? Where would I have been if I had not taken that bus? Yann LeCun Yann LeCun says it well in his position paper ( I quote: "Using such world models, animals can learn new skills with very few trials. They can predict the consequences of their actions, they can reason, plan, explore, and imagine new solutions to problems. Importantly, they can also avoid making dangerous mistakes when facing an unknown situation." The first use of the term World Model in deep policy learning is attributed to hardmaru & Jürgen Schmidhuber: In their seminal paper, an agent masters shooting skills in the popular game Doom (demo below) by learning in imagination, using an internal world model as a "physics simulator". To put in a simple Python math formula, world model learns a function F(s[0:t-1], a) -> s[t:], which takes as input the observed past and current action, and outputs plausible future states. Now the definition of World Model in Tegmark's paper seems to be about predicting GPS coordinates and time eras. I see this as just a classification task with no causal learning and simulation going on. You cannot make meaningful interventions against that model, nor can you optimize any decision making in a closed feedback loop. As for the "space & time neurons", I think they are most similar to the "sentiment neuron" that OpenAI published in 2017: Predicting GPS is conceptually no different from predicting sentiment in my opinion. I don't think their experimental results are wrong - just that their conclusion is on shaky grounds. I welcome any debate! Paper link:

Jim Fan

594,014 次观看 • 2 年前

Solidus Ai Tech Announcement As we approach the end of the year, we want to share a clear and considered update with our community. The final weeks of the year are shaped by holiday periods, Christmas breaks, and New Year’s downtime. Across the industry, attention drops, teams are offline, and meaningful engagement slows. Launching major products during this window would limit visibility, momentum, and adoption. After internal alignment across the entire team, we have collectively agreed to prioritize impact over timing. We are fully aligned on our goals, our roadmap, and the long-term direction of Solidus AI Tech. 2025 has been a challenging year for many. High volatility driven by geopolitical issues, increased institutional control over markets, and liquidity being pulled from altcoins have tested builders and communities alike. Through it all, our focus has remained unchanged: building real infrastructure, real products, and a sustainable ecosystem. For this reason, our upcoming products, the Compute Marketplace, Agent Forge 2.0, and Vision Makers, are now scheduled for release in early 2026, at a time when attention, participation, and momentum are back at full strength. This ensures each release receives the focus, usage, and traction it deserves. We would like to take this opportunity to wish everyone a Merry Christmas and a happy holiday season. Thank you for your continued support, patience, and belief in what we are building. Further updates will be shared in due course.

AITECH CLOUD NETWORK

51,425 次观看 • 7 个月前

Season 2 of Valiants: Tap-Tap is here!🌟 It’s time to dive into the new and exciting world of Valoria! Here’s everything you need to know: Now available here! 🔥 New Points Store Your efforts in Season 1 have paid off. Now, you can use your store points to redeem amazing rewards. What will you find? Surprise boxes containing exclusive items in Valiants: Tap-Tap and Valiants: Arena, NFTs that you can trade in the future, and a portion of the $VGN Airdrop allocation. Important: For now, the boxes can only be purchased, but soon you’ll be able to open them to discover what’s inside. Stay tuned for updates so you don’t miss anything! 🕒 🎁 Wild Spin - Spin and Win Introducing the new **Wild Spin**, where each spin could change your fate. Spins will be awarded based on the activity and performance of your friends with a good User Score. So, stay active and keep connecting with your teammates to maximize your chances! What can you win? Experience, unlocks, and even USDT prizes that you can withdraw directly to your airdrop wallet. Tip: Use your USDT winnings to purchase additional spins and keep the wheel turning. How far will your luck take you? 🎰 💥New Valiants and Items The battle in Valoria heats up with the arrival of new Valiants and accessories. These exclusive items will allow you to explore new strategies and challenge your opponents like never before. 📋 Patch Notes Not only are we introducing new features, but we’ve also made several improvements and adjustments to provide you with a smoother and more satisfying gaming experience. Here are all the details: - Daily Combo Changes: The combo is now linear, meaning the challenge has increased. Can you keep up the streak? This change is designed to reward the most skilled and dedicated players. - Daily Login Update: Starting from day 10, the rewards get even better. Don’t miss a single day to maximize your benefits! We’ve also adjusted the progression so that each login brings you closer to more significant rewards. - Complete UI Redesign: We’ve revamped the entire user interface to make it more intuitive and visually appealing. Additionally, you can now choose between two visual themes: the serenity of Kai or the energy of Mimi. We’ve even added some music to accompany your adventure! These improvements are designed to make your time in Valoria even more enjoyable. We hope you love them as much as we do! ✨Don’t miss out on this new adventure! Explore all the new features in Season 2 and discover how to dominate the new world of Valoria. Remember, every decision can bring you one step closer to victory. See you in the game!🔥

Valiants

81,606 次观看 • 1 年前

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Anshu

19,931 次观看 • 1 个月前

🚀 Dive into MetaMask Season 1 with Linea: new on-chain quests are live in daGama! We’re excited to announce that new quests from Linea.eth and MetaMask 🦊 are now live on daGama’s Questboard! Dive into the MetaMask Season 1 campaign with the major Layer 2 network Linea, explore the ecosystem in depth, and earn up to 700 daGama XPs 💫 🌐 What is Linea? Linea is zkEVM Layer2 network bringing Ethereum's security, scalability, and developer tools to millions of users. Built by Consensys, it offers fast, low-cost transactions with full EVM compatibility. Nearly 45% of all MetaMask swaps now happen on Linea, showing how quickly it has become a go-to destination for DeFi, perpetuals, gaming, and everyday on-chain activity. 🦊 What is MetaMask? MetaMask is a trusted Web3 wallet and browser extension that empowers users to explore DeFi, NFTs, and dApps across multiple blockchains while maintaining full control of their private keys. With over 30 million monthly active users and 100 million yearly users worldwide, it remains one of the most secure and widely adopted crypto wallets in 2025. Now you can join MetaMask Season 1 with Linea via daGama Questboard & get XPs in both campaigns: 1️⃣ Explore MetaMask Season 1 Download MetaMask and add your existing address to the Rewards tab in the daGama mobile app: Reward: 100 XP 2️⃣ Linea swaps Switch to the Linea chain, open the “Trade” tab, and swap any available tokens (min. $100). After a successful swap, leave your transaction ID in the answer field. Reward: 300 XP 3️⃣ Explore perps on MetaMask Start perpetual trading on MetaMask using $LINEA (any amount is acceptable), and submit your transaction ID. Reward: 300 XP ⚠️ Perpetual trading involves high risks. Participate responsibly. ⏳ Don’t miss your chance to boost your XP and climb the lead.

daGama

72,161 次观看 • 7 个月前