Browsing: LLM

Zyphra, the San Francisco-based AI lab behind the ZAYA1 model family, released ZAYA1-8B-Diffusion-Preview — a preview of its early work in diffusion-language models. The release demonstrates…

Poetiq has just published some very interesting results showing its Meta-System reached a new state-of-the-art on LiveCodeBench Pro (LCB Pro), a competitive coding benchmark, by automatically…

When you fine-tune large language models (LLMs) with Amazon SageMaker AI while using Databricks Unity Catalog, you might face unique challenges like how to maintain strict…

Pre-training large language models is expensive enough that even modest efficiency improvements can translate into meaningful cost and time savings. Nous Research is releasing Token Superposition…

The EU AI Act requires organizations fine-tuning large language models (LLMs) to track computational resources measured in floating-point operations (FLOPs) to determine compliance obligations. As customers…

Modern large language models are no longer trained only on raw internet text. Increasingly, companies are using powerful “teacher” models to help train smaller or more…

Large language models are no longer just about scale. In 2026, the most important LLM research is focused on making models safer, more controllable, and more…

banner(“Part 5 — Streaming”) mem.attribution(entity_id=”[email protected]”, process_id=”personal-assistant”) stream = client.chat.completions.create( model=MODEL, messages=[{“role”: “user”, “content”: “In two sentences, what do you remember about me?”}], stream=True, ) print(“[stream] “,…

if proxy_alive(): print(“\n[10] Mixed 10-prompt workload…”) workload = [ “Capital of France?”, “Read foo.py”, “Type hint for a list of dicts”, “Lowercase: HELLO”, “One-sentence summary of…

  # Introduction  JSON is great for APIs, storage, and application logic. But inside large language model (LLM) pipelines, it often carries a lot of token overhead…