Model · Image and text to text
Qwen
As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next. This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale. The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces: For…
Open weights
other
180B parameters
262,144 tokens
transformers
This model has had its safety alignment substantially removed via abliteration (orthogonalizing the refusal direction out of the residual stream). It will comply with harmful, unethical, or illegal requests the original Qwen3.8-Flash-Next would refuse. Released strictly for legitimate research — interpretability, AI-safety / refusal-mechanism study, red-teaming, and robustness evaluation. You assume full responsibility for how you use it and everything it generates; add your own safety and moderation layers before any deployment. Use must comply with the Apache 2.0 License inherited from the base model and all applicable law. The authors accept no liability for misuse. - A Blackwell GPU…
Access requested at publisher
apache-2.0
180B parameters
transformers
flagship of the Darwin family — #1 on AIME 2026, HMMT Feb 2026, GPQA Diamond, MMLU-Pro, MMMU-Pro, LEXam and LEXam-hard, and a model that gets better by learning from its own verified work. Scores as listed on the Hugging Face official benchmark leaderboards (self-reported by each model's publisher). = #1 on that leaderboard. "—" = not reported. Darwin is VIDRAFT's measurement-driven reasoning model family — 50+ official models, 400+ community derivatives, and now two places in the GPQA Diamond top 3 (Darwin-180B-RSI #1 · Darwin-397B-ZTC #3). Darwin treats a strong open model as a parent. It measures where the parent is weak, and strengthens exactly those parts — instead of re-training…
Open weights
other
180B parameters
262,144 tokens
transformers
A sensitivity-guided, mixed-precision MLX conversion of Qwen/Qwen3.8-Flash-Next, rebuilt directly from the official BF16 checkpoint with the model's matching native MTP block preserved. oQ3 uses a 3-bit affine base and spends additional precision on sensitive modules. Layer sensitivity was measured with a validated quantized calibration proxy, while every released weight was quantized from the official BF16 checkpoint. The result is a compact model with 746 higher-precision module overrides rather than a uniform 3-bit layout. The upstream tokenizer, current chat template, vision processor, generation configuration, licence, and native MTP configuration are retained. In a compatible oMLX…
Open weights
other
180B parameters
262,144 tokens
mlx
TH
Model · Image and text to text
Tai Hua
As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next. This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale. The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces: For…
Open weights
other
180B parameters
262,144 tokens
transformers
A
Model · Image and text to text
Ahmed
As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next. This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale. The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces: For…
Open weights
other
180B parameters
262,144 tokens
transformers
