A REX checkpoint of Intern-S2 with WMDP-Cyber knowledge relocated into 8 removable experts per MoE layer (264 experts in total). Excising these experts together with their router rows yields A3, a standard 256-expert model with that knowledge removed. This repository holds the expanded checkpoint F3: the REX model after 250M tokens of standard full-parameter SFT, with the removable experts still attached. A3 differs from F3 only by excision. CPU only and about 1 min, with at least 16 GiB RAM. Disk: 70 GiB for F3 plus 69 GiB for A3../A3 must not exist beforehand, and expertexpansionmanifest.json must stay in./F3. Load./A3 with the model class named in its config.json. Intern-S2 ships a…
Open weights
apache-2.0
37.8B parameters
262,144 tokens
transformers
A REX checkpoint of Qwen3.5-35B-A3B with WMDP-Cyber knowledge relocated into 8 removable experts per MoE layer (264 experts in total). Excising these experts together with their router rows yields A3, a standard 256-expert model with that knowledge removed. This repository holds the expanded checkpoint F3: the REX model after 250M tokens of standard full-parameter SFT, with the removable experts still attached. A3 differs from F3 only by excision. CPU only and about 1 min, with at least 16 GiB RAM. Disk: 69 GiB for F3 plus 67 GiB for A3../A3 must not exist beforehand, and expertexpansionmanifest.json must stay in./F3. Load./A3 with the model class named in its config.json. Scores in %, ±…
Open weights
apache-2.0
37B parameters
262,144 tokens
transformers
A REX checkpoint of Qwen3.5-35B-A3B with WMDP-Bio knowledge relocated into 8 removable experts per MoE layer (264 experts in total). Excising these experts together with their router rows yields A3, a standard 256-expert model with that knowledge removed. This repository holds the expanded checkpoint F3: the REX model after 250M tokens of standard full-parameter SFT, with the removable experts still attached. A3 differs from F3 only by excision. CPU only and about 1 min, with at least 16 GiB RAM. Disk: 69 GiB for F3 plus 67 GiB for A3../A3 must not exist beforehand, and expertexpansionmanifest.json must stay in./F3. Load./A3 with the model class named in its config.json. Scores in %, ±…
Open weights
apache-2.0
37B parameters
262,144 tokens
transformers
A REX checkpoint of Intern-S2 with WMDP-Bio knowledge relocated into 8 removable experts per MoE layer (264 experts in total). Excising these experts together with their router rows yields A3, a standard 256-expert model with that knowledge removed. This repository holds the expanded checkpoint F3: the REX model after 250M tokens of standard full-parameter SFT, with the removable experts still attached. A3 differs from F3 only by excision. CPU only and about 1 min, with at least 16 GiB RAM. Disk: 67 GiB for F3 plus 65 GiB for A3../A3 must not exist beforehand, and expertexpansionmanifest.json must stay in./F3. Load./A3 with the model class named in its config.json. Intern-S2 ships a custom…
Open weights
apache-2.0
36.1B parameters
262,144 tokens
transformers
Model · Image and text to text
Qwen
Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. This release delivers substantial upgrades, particularly in For more details, please refer to our blog post Qwen3.6-35B-A3B. Empty cells (--) indicate scores not available or not applicable. For streamlined integration, we recommend using Qwen3.6 via APIs. Below is a guide to use Qwen3.6 via OpenAI-compatible API. Qwen3.6 can be served via APIs with popular inference frameworks. In…
Open weights
apache-2.0
36B parameters
262,144 tokens
transformers
Model · Image and text to text
Qwen
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. For more details, please refer to our blog post Qwen3.5. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. Empty cells (--) indicate scores not yet available or not applicable. Empty cells (--) indicate scores not…
Open weights
apache-2.0
36B parameters
262,144 tokens
transformers
