Intern-S2-Mobius-FP8

Intern-S2-Mobius

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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 while producing markedly shorter reasoning traces and higher request throughput, leading to nearly 4x end-to-end inference speedup in the reported evaluation.

• Strong general and scientific performance. Intern-S2-Mobius improves the reported average score over Qwen3.5-35B on general reasoning benchmarks, and shows large gains on scientific tasks such as Biology-Instructions, Mol-Instructions, and MolecularIQ.

<figure> <img src="./figs/efficiency.png" alt="Mobius inference efficiency"> <figcaption>Fig1: Inference efficiency on reasoning benchmarks. Intern-S2-Mobius improves request throughput over the Transformer baseline while maintaining strong reasoning performance, with gains largely coming from shorter, more compact reasoning traces.</figcaption> </figure> <figure> <img src="./figs/chain-of-thought.png" alt="chain of thought"> <figcaption>Fig2: The average output length of Mobius continual pre-trained from Qwen3.5.</figcaption> </figure>

Performance

We evaluate the Intern-S2-Mobius on various benchmarks, including general datasets and scientific datasets. We report the performance comparison with Qwen3.5-35B below. We use the OpenCompass to evaluate all models. For text benchmarks, Intern-S2-Mobius is evaluated with a maximum inference length of 64K tokens on MMLU Pro, SimpleQA, and HLE, and 128K tokens on the remaining text benchmarks.

<figure> <img src="./figs/performance.png" alt="performance"> <figcaption>Fig3: Performance comparison across general and scientific benchmarks. The higher score in each row is highlighted in <strong>bold</strong>.</figcaption> </figure> <figure> <img src="./figs/case-study.png" alt="case study"> <figcaption>Fig4: Step-aligned comparison between Intern-S2-Mobius-35B and Qwen3.5-35B on a linear-algebra multiple-choice question. Both models select the correct answer (Option C). Token counts are computed using the Qwen3.5-35B tokenizer. Mobius completes the same reasoning steps with fewer tokens, which mainly benefits from the model's elimination of repeated derivation and checks.</figcaption> </figure>

Quick Start

The Intern-S2-Mobius release is a 35B model stored in bfloat16 weight format. This guide provides deployment examples for the following configurations:

• MTP speculative decoding (Recommended)
• Basic serving without MTP

NOTE: The commands below are reference configurations. Inference frameworks are under active development, so use the latest framework documentation and your local validation results when tuning production deployments.

Intern-S2-Mobius can be deployed using any of the following LLM inference frameworks:

• LMDeploy
• Transformers
• vLLM
• SGLang

Sampling Parameters

We recommend using the following hyperparameters to ensure better results

top_p = 1
top_k = 50
min_p = 0.0
temperature = 0.8

LMDeploy

Use the latest LMDeploy with Intern-S2-Mobius support. The examples below use single-GPU serving.

• Serving With MTP (Recommended)

lmdeploy serve api_server \
    internlm/Intern-S2-Mobius \
    --trust-remote-code \
    --backend pytorch \
    --tp 1 \
    --speculative-algorithm qwen3_5_mtp \
    --speculative-num-draft-tokens 4 \
    --dtype bfloat16 \
    --max-batch-size 64 

• Basic Serving Without MTP

lmdeploy serve api_server \
    internlm/Intern-S2-Mobius \
    --trust-remote-code \
    --backend pytorch \
    --dtype bfloat16 \
    --tp 1 

Transformers

Use a recent Transformers version with remote-code loading enabled.

• Basic Inference

import torch
from transformers import AutoModelForImageTextToText, AutoTokenizer

model_path = "internlm/Intern-S2-Mobius"

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
).eval()

messages = [
    {"role": "user", "content": "Give me a short introduction to Intern-S2-Mobius."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=True,
        temperature=0.8,
        top_p=1,
    )

response_ids = output_ids[0][inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(response_ids, skip_special_tokens=True))

vLLM

Use the latest vLLM Docker image or source build with Intern-S2-Mobius support.

• Serving With MTP (Recommended)

vllm serve \
    internlm/Intern-S2-Mobius \
    --trust-remote-code \
    --tensor-parallel-size 2 \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --spec-method mtp \
    --spec-tokens 4

• Basic Serving Without MTP

vllm serve \
    internlm/Intern-S2-Mobius \
    --trust-remote-code \
    --tensor-parallel-size 2 \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder 

SGLang

Use the lmsysorg/sglang:dev Docker image or a recent source build with Intern-S2-Mobius support. See the SGLang cookbook for Docker commands, verified deployment recipes, benchmarks, and usage examples.

• Serving With MTP (Recommended)

sglang serve \
    --model-path internlm/Intern-S2-Mobius \
    --trust-remote-code \
    --tp 2 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder \
    --speculative-algorithm NEXTN \
    --speculative-num-steps 3 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 4

• Basic Serving Without MTP

sglang serve \
    --model-path internlm/Intern-S2-Mobius \
    --trust-remote-code \
    --tp 2 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder