Today, we’re announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we’re excited to introduce its most powerful variant first: Qwen3-Coder-480B-A35B-Instruct. featuring the following key enhancements: — Significant Performance among open models on Agentic Coding, Agentic Browser-Use, and other foundational coding tasks, achieving results comparable to Claude Sonnet. — Long-context Capabilities with native support for 256K tokens, extendable up to 1M tokens using Yarn, optimized for repository-scale understanding. — Agentic Coding supporting for most platform such as Qwen Code, CLINE, featuring a specially designed function call format. Qwen3-480B-A35B-Instruct has the following features: — Type: Causal Language Models — Training Stage: Pretraining & Post-training — Number of Parameters: 480B in total and 35B activated — Number of Layers: 62 — Number of Attention Heads (GQA): 96 for Q and 8 for KV — Number of Experts: 160 — Number of Activated Experts: 8 — Context Length: 262,144 natively. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and…
Модальности:
Генерация текста
Области применения:
Диалог / чат Генерация кода Следование инструкциям
Задача: Генерация текста
Автор: NVFP4
Теги: Model Optimizer, qwen3_moe, nvidia, ModelOpt, Qwen3, quantized, FP4, conversational
Лайков: 3 | Загрузок: 50
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
