alibaba-qwen

Alibaba's Qwen team has developed Future-KL Influenced Policy Optimization (FIPO), a training algorithm that assigns different weights to tokens based on their influence on subsequent reasoning steps, rather than treating all tokens equally. Testing on Qwen2.5-32B-Base showed reasoning chains double from ~4,000 to 10,000+ tokens, with AIME 2024 accuracy improving from 50% to 58%, outperforming Deepseek-R1-Zero-Math-32B (47%) and OpenAI's o1-mini (56%). The team plans to open-source the system.

Alibaba's Qwen team has released Qwen3.5-0.8B, an 800-million-parameter multimodal model designed for resource-constrained environments. The model handles image-text-to-text tasks and is distributed under Apache 2.0 licensing, making it freely usable for commercial applications.

Alibaba's Qwen team released Qwen3.5-35B-A3B-FP8 on Hugging Face, a quantized version of their 35-billion parameter multimodal model. The FP8 quantization reduces model size and memory requirements while maintaining the base model's image-text-to-text capabilities. The model is compatible with standard Transformers endpoints and Azure deployment.

Alibaba Qwen has released Qwen3.5-27B, a 27-billion parameter model capable of processing both images and text. The model is available under an Apache 2.0 open license and is compatible with standard transformer endpoints.

Alibaba has released Qwen3.5-35B-A3B, a 35-billion parameter multimodal model capable of processing images and text. The model is published under an Apache 2.0 license and available on Hugging Face with Transformers and SafeTensors format support.

Alibaba has released the Qwen 3.5 series, an open-source model that claims performance comparable to frontier proprietary models while running on commodity hardware. The release signals a shift in AI model economics, offering enterprises lower inference costs and greater deployment flexibility than closed alternatives.