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Hi, same as the title, I am currently trying to begin with some research on clustering using LLMs. So my requirement is as follows: I will be given some 100 document files, the end goal is to have clusters in such a way that documents with similar procedures or content should be clubbed under…
AI Engineering from Scratch is an MIT-licensed curriculum: 523 lessons across 20 phases, from linear algebra and backprop to transformers, LLMs, agents, and production serving. The code is stdlib-first, so you see every step instead of calling a library.
Saw this job posting recently from Deepmind where the minimum requirements are a Bachelor's and 2 years of experience: https://www.google.com/about/careers/applications/jobs/results/120898404719960774-optimization-research-engineer-deepmind
OpenTrainDNN is an open-source, client-side web application designed to render the step-by-step training mechanics of deep neural networks in real-time.
Help: Project l'm building a shelf audit tool. A photo goes through YOLO, which crops each product, and then I embed the crop and search a small gallery of reference photos to get the SKU. New products should be addable by just dropping in photos, no detector retrain. Detection is basically fine.
Why I'd invest in post-training existing open-weight LLMs, with Fireworks' Ember-1 as an example of more token-efficient reasoning.
I was reading a paper that surveyed the field of neural architecture search, where it said within 5 years, around 3000+ new models were proposed. The amount of compute and resources spent on this is absolutely astronomical.
The opponent plans by simulation: every second it scores each candidate play by running the match 10 seconds ahead in the engine. Our PPO agent learned to park its Cannon behind its own King. Losing a building in a fight cost reward, and letting it decay cost nothing, so it found the loophole.
Recently I have been working on image attachment integration for my VEX agent runtime, and I came up with an idea for a significant optimization step. Typically images attached to LLM context as raw bytes (base64 encoded string or publicly accessible URL), and the VLM processes image data by…
We published Intern-Decision multi-modal model family (0.8B, 2B, 4B), and the 4B model outperformed Jev 1.13.0. https://huggingface.co/collections/internlm/intern-decision https://github.com/InternLM/Intern-Decision Performance Testing on RTX 4090, we find local deployment is fast enough to enable…
Google has published benchmarks for GKE Pod snapshots, reporting up to 89% lower startup latency and a 70B model loading in 37 seconds. The feature checkpoints CPU and GPU memory through gVisor into Cloud Storage.
In this project I wanted to see if any interesting emergent behaviors would appear if we trained two agents to play a streetfighter-like game using RL. Maybe obvious in retrospect, but the agents are really good at reward hacking.
My AI detectors flagged many genuine reviews, and filtering them made the sentiment model less accurate. The post AI Slop Is Already in Your Training Dataset. I Tested Three Ways to Spot It. appeared first on Towards Data Science.
Mallika Rao explains that the true complexity of adaptive recommendation systems lies outside model architecture. She discusses how real-time feedback loops, retrieval freshness, multi-stage orchestration, and end-to-end latency budgeting enable systems to continuously learn and evolve in…
Apple has published the design of Reference Image, an iPhone 18 Pro camera mode that signs pixel data at the sensor and develops it in Private Cloud Compute under an Apple signature.
scikit-learn is one of the most widely used tools in data science, counting over 200 million downloads every month. You may know it as the Python library, but behind the code there is a global community that develops, maintains, and sustains it for the benefit of all data scientists all over the…
Notes on MiMo-V2.6 Pro's GQA and sliding-window attention, agent training tasks, reward signals, and large RL batches.
A short note on Jev's generalization, possible encoder-style architecture and training, and Choice and Noul API examples.
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