Ryujin 3.5 «2027»

prompt = "Explain the significance of the Dragon God in Shinto mythology." inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

For developers, the lesson is clear: The era of dense LLMs is sunsetting. Have you run an MoE model locally? How does your experience compare to dense models like LLaMA? Share your benchmarks in the comments below. ryujin 3.5

Note: As of my latest knowledge cutoff, "Ryujin 3.5" is not an official release from major AI labs (OpenAI, Anthropic, Google, Meta, Mistral). However, given naming conventions in the open-source community (often inspired by Japanese mythology: Ryujin = Dragon God), this post is written as a forward-looking or speculative analysis of what such a model would represent, particularly in the context of Mixture-of-Experts (MoE) architecture and efficiency-focused LLMs. In the rapidly evolving world of Large Language Models (LLMs), bigger isn't always better. While tech giants battle over万亿-parameter monsters, a new class of "surgical" models is emerging. Enter Ryujin 3.5 —a hypothetical but highly plausible next step in efficient, Mixture-of-Experts (MoE) architecture. prompt = "Explain the significance of the Dragon

| Benchmark | Ryujin 3.5 (6B active) | LLaMA 3 (8B dense) | GPT-3.5 Turbo | | :--- | :--- | :--- | :--- | | | 72.4% | 66.5% | 69.8% | | HumanEval (Code) | 68.2% | 62.1% | 64.5% | | Inference Speed (t/s) | 110 t/s | 85 t/s | 90 t/s | | VRAM (4-bit) | 18 GB | 6 GB | N/A (Closed) | Share your benchmarks in the comments below