Alibaba Qwen发布了Qwen3.8-Flash-Next，一个开放权重模型，支持多种微调工具，成本效益高，值得一看。
Alibaba Qwen发布Qwen3.8-Flash-Next，一个实验性开放权重模型，预览Qwen4架构。支持使用NVIDIA NeMo AutoModel和NeMo RL微调，与TokenSpeed兼容。Qwen3.8-Flash生产版即将通过QwenCloud API推出，每1M输入令牌0.16美元，每1M输出令牌0.47美元。模型参数125B，N-gram嵌入51B，每令牌激活6B。训练和推理成本大幅降低，在多个基准测试中表现优异。
Congrats to @Alibaba_Qwen on releasing Qwen3.8-Flash-Next, an experimental open-weight model that pr...
Congrats to @Alibaba_Qwen on releasing Qwen3.8-Flash-Next, an experimental open-weight model that previews the Qwen4 architecture. We’ve got Day 0 support to fine-tune with NVIDIA NeMo AutoModel and NeMo RL, plus recipes to run it with @sgl_project , @vllm_project and @lightseekorg TokenSpeed. Get started: nvda.ws/4wV7yXp Qwen @Alibaba_Qwen ⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight! The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens. 125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency. What's new: 🥳 - Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4. - Dramatically lower training and inference costs: trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board with especially strong gains in coding and office tasks. - Strong performance: scoring 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision (with CI). - 262K native context, extensible to 1M with YaRN. We’re also releasing the weights for Qwen3.8-Flash-Next, giving the community an early look at the new architecture we’re exploring for Qwen4.🚀 We can't wait to see what you build with Qwen3.8-Flash!👀👇 - Bl qwen.ai/blog?id=qwen3.… FLgJ - Technical Repo github.com/QwenLM/Qwen3.8… IkQO - Hugging Fa huggingface.co/Qwen/Qwen3.8-F… AABt - ModelSco modelscope.cn/models/Qwen/Qw… NuFG 🔗 View Quoted Tweet 💬 4 🔄 10 ❤️ 49 👀 3708 📊 9 ⚡