Exploring AI Learning, the specifics of Large Language Models, including architecture, prompt engineering, fine-tuning, RAG, and their application in text-based tasks.
Text generation has a speed problem that no amount of bigger hardware fully solves. Language models write one token at a time, and each token needs a full pass through the network. Output twice as long, wait twice as long. DSpark is DeepSeek’s attempt to break that pattern, and it does so without retraining a … Read more
At 3 A.M., the last thing anyone expects from a chatbot is a soft intervention. Not a syntax error. Not a refusal. A wellness nudge. “You should get some sleep.” Maybe drink water too. The internet did what the internet does best, it turned this into a meme. Is Claude becoming a worried parent? Is … Read more
For most of the modern AI boom, text generation has had one very good trick: predict the next token, then do it again, and again, and again, until the machine either writes a sonnet or apologizes for being unable to help with your toaster. Diffusion language models ask a different question. What if language didn’t … Read more
The weird thing about vision models is that they can often describe an image beautifully, then fall apart when asked to do something a five-year-old does with a finger. Count the bears on the ground. Trace the line from the crown icon. Navigate the maze. Don’t hallucinate a shortcut through a wall. That is where … Read more
An AI operating system sounds, at first, like the sort of phrase that gets shouted over synth music in a keynote and forgotten by lunch. Then you read Meta and KAUST’s Neural Computers paper and realize they mean it literally. Not “an assistant for your desktop.” Not “a smarter shell.” An AI operating system where … Read more
Self-Distillation is one of those ideas that sounds suspicious on first contact. Train a model on its own raw outputs? No verifier, no teacher, no reward model, no reinforcement learning, no execution sandbox? That usually sounds like a fast route to elegant nonsense. Which is why this Apple AI research paper is so interesting. It … Read more
KV Cache Compression: Recall vs. Memory Needle-in-Haystack benchmark · Llama-3.1-8B-Instruct · context up to 104k tokens Best recall 0.997 TurboQuant = full precision Memory at 3.5-bit 6x smaller KV cache GPU speedup 8x on H100 at 4-bit Needle-in-Haystack recall score KV cache size (bits) Tested on Llama-3.1-8B-Instruct · Needle-in-Haystack benchmark · context up to 104k … Read more