Intern-S2-Mobius

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What Intern-S2-Mobius is

Intern-S2-Mobius 💻Github Repo • 🤗Model Collections • 🌳Arch Space Introduction We introduce Intern-S2-Mobius, a 35B foundation model built on the Mobius-v0 architecture realized by Xtuner and LMDeploy. Instead of binding knowledge storage and reasoning computation layer by layer as in conventional Transformer models, Mobius organizes knowledge into a globally shared Memory and lets multiple Reasoners iteratively query and refine hidden states against this shared repository. This knowledge-reasoning separation gives Intern-S2-Mobius two native capabilities: Backward Residual Connection, where reasoning stages can access knowledge beyond their local layer hierarchy, and Dynamic Latent Reasoning, where deliberation, refinement, and multi-token prediction are internalized into high-density continuous states. Continual-pretrained from Qwen3.5-35B and further post-trained with SFT and RL, Intern-S2-Mobius preserves strong downstream capability while achieving substantially higher end-to-end inference efficiency, with nearly 4x speedup reported in the technical report. Features - Knowledge-reasoning decoupled architecture. Intern-S2-Mobius separates knowledge vectors from reasoning operators by replacing layer-bound FFN knowledge storage with a globally shared Memory. This gives each Reasoner access to a broader knowledge space and improves knowledge compression compared with a standard Transformer layout. - Backward Residual Connection. Through shared Memory, shallow and deep reasoning stages can access knowledge across the model rather than relying only on forward layer-wise information flow. This enables more flexible cross-layer knowledge composition and helps the model synthesize useful information in fewer reasoning steps. - Dynamic Latent Reasoning. Mobius refines continuous hidden states through recurrent latent iteration before decoding. This internalizes part of the deliberation process, reduces reliance on long visible chain-of-thought, and dynamically allocates computation to different tokens. - Higher inference efficiency with concise reasoning. On reasoning benchmarks, Intern-S2-Mobius reaches comparable or stronger scores than the Qwen3.5-35B baseline whi

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

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How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys intern-s2-mobius for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (intern-s2-mobius below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"intern-s2-mobius","messages":[{"role":"user","content":"Hello"}]}'

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