Roshan Sumbaly’s Post

𝐋𝐥𝐚𝐦𝐚 3.3 70𝐁 𝐢𝐬 𝐇𝐞𝐫𝐞! 🌍 A 70B multilingual model supporting 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. 🔑 128K token context window. 📚 Trained on over 15 trillion tokens with knowledge updated to December 2023. 💻 88.4% on HumanEval for code generation. 🧮 91.1% on Multilingual Mathematics Problem Solving (MGSM). 🚀 Significant improvements over Llama 3.1. If you need the extra performance, Llama 3.3 on Groq @2300 tokens/second: https://lnkd.in/gjT6UvYK

Vid2Persona: talk to person from video clip A fun project over the last week with Sayak Paul. It has a simple pipeline from extracting traits of video characters to chatting with them. Under the hood, this project leverages the power of both commercial and open source models. We used Google's Gemini 1.0 Pro Vision model to understand the video content directly, then we used HuggingFaceH4/zephyr-7b-beta model to make conversation! Try it Hugging Face Space and let us know what you think. https://lnkd.in/d5qw5jmx The space application is a dedicated implementation for ZeroGPU environment + Hugging Face Inference API with PRO account. If you wish to host it on your own environment, consider duplicate the space or run locally with the project repository : https://lnkd.in/dWagsZdy

Inbox v1 is ready in Midday 🎉 Drag-and-drop files or use your unique email to reconcile everything, saving time and improving accuracy. * Email provided by Postmark 📥 * Uploaded to Supabase storage 🗄️ * Background job via Trigger.dev 🔄 * Vercel AI SDK 🤖 * OCR extraction 👀 An in-depth tech blog coming soon. Share your questions you may have

Folks, I am sorry to say Dronc is dead. I had to put my local LLM on oncology into storage as I stay with my family for the summer. Unfortunately for my family, they are going to have to deal with me puttering around the office during my free time creating another LLM on my local laptop. I don't know what your process is but my kids won't stop commenting on my pacing and making jazz scat noises (link here for reference, and of course I don't sound this good when I do it: https://shorturl.at/3oUaX) I don't have a pithy name for it yet but what I want to see is the efficacy of different models with the same data set for a given task. What I expect to see is a see saw effect, where exchanging different models will find some improvements in certain aspects of completing the task while deficient in others. I'm probably going to end up just proving the point that fine tuning, RAG, and data segmented through machine learning is necessary - but I want to tinker. If anyone else out there has melted their laptop trying to tinker running local LLMs, please let me know if have any other fun or novel projects to attempt! On the other hand if you're looking to experiment yourself, highly recommend the below Reddit threads: https://lnkd.in/d_esypdt https://lnkd.in/dY36-b-z

One Llama, two Llamas, three Llamas… 405B Llamas?! Meta’s Llama 3.1 brings top-tier performance to the open-source community—a game-changer for innovation. With Llama 3.1 405B, a multi-node cluster is a must for full-precision inferencing. Read vLLM's complete deployment guide using Lambda’s 1-Click Clusters: https://lnkd.in/g3qrQh4i Get your on-demand cluster today: https://lnkd.in/eu9xieKe

Day 5 of the '30 Days of Graphlit' is here! In this example, we show how to use Graphlit for competitive intelligence and analyzing mentions on Reddit. By ingesting posts from r/Anthropic, and enabling entity extraction, we can then filter on any Reddit posts that mentioned Google. Notebook: https://lnkd.in/gce-Af5i Colab: https://lnkd.in/ghKRSrad

You're not finished counting Llamas! 🦙 3.1 series brings improved reasoning capabilities, 128K token context... and a 405B version. vLLM produced a guide for inferencing the 405B heavy-hitter - leveraging Lambda 1-Click Clusters. Multi-node is the way to go if you want full precision!

🌟 Day 12: Array Adventures in DSA Land! 🚀 Today, I dove deeper into the world of Data Structures and Algorithms by conquering four intriguing array challenges. Here's a snapshot of my journey: 1. Zeroes to the End: I arranged all zeroes at the end, keeping the rest of the numbers in their original order. 2. Element Hunt: I quickly found a specific number in the array. 3. Array Merge: I combined two sorted arrays into one, avoiding any duplicates. 4. Common Elements: I discovered which numbers appear in both arrays. I’m thrilled to see how these challenges improve my DSA skills! #100DaysOfCode #DSAJourney #SimpleCoding 🌟

Athena has first class support for research intensive workflows. Think like product research, competitive analysis, sentiment analysis. It's accessible via chat, full scale research reports, or when you need even more control you can use Jupyter notebooks and the Athena SDK to get exactly what you need. Here's a demo of that third option and a line by line walk through of the code to do it. Athena is in private beta. DM me if you want early access!

🎉 The wait is over – we’re live on Product Hunt! 🎉 Introducing our Data Labeling Platform, built to simplify your annotation workflow: ✅ Computer vision focus (for now 😉) ✅ API access ✅ Free trial ✅ Cost calculator Perfect for ML engineers, AI-driven businesses, and academic researchers. 🥂 You're invited to our comment party there: (link in the comment)