Browsing: Qwen

Most AI models today are not designed for sustained, multi-step autonomous execution. Tasks like running hundreds of iterative code modifications, or chaining tool calls across hours…

Simultaneous interpretation is one of the harder problems in applied AI. You’re asking a model to translate speech before the speaker has finished a sentence. Every…

Large language models are remarkably capable, yet frustratingly opaque. When a model misbehaves — generating responses in the wrong language, repeating itself endlessly, or refusing safe…

The race to make large language models faster and cheaper to run has largely been fought at two levels: the model architecture and the hardware. But…

Alibaba’s Qwen Team has released Qwen3.6-27B, the first dense open-weight model in the Qwen3.6 family — and arguably the most capable 27-billion-parameter model available today for…

class QwenChat: def __init__(self, model, processor, system=None, tools=None): self.model, self.processor = model, processor self.tokenizer = processor.tokenizer self.history: list[dict] = [] if system: self.history.append({“role”: “system”, “content”: system})…

The open-source AI landscape has a new entry worth paying attention to. The Qwen team at Alibaba has released Qwen3.6-35B-A3B, the first open-weight model from the…

The landscape of multimodal large language models (MLLMs) has shifted from experimental ‘wrappers’—where separate vision or audio encoders are stitched onto a text-based backbone—to native, end-to-end…

Mobile World Congress in Barcelona might be a European tech show, but for the past few years, the event has largely been dominated by Chinese phone…

Alibaba’s Qwen team has released the Qwen3.5 Small Model Series, a collection of Large Language Models (LLMs) ranging from 0.8B to 9B parameters. While the industry…