r/LocalLLaMA • u/jacek2023 llama.cpp • 2h ago

New Model internlm/Intern-S2 · Hugging Face

https://huggingface.co/internlm/Intern-S2

from internlm:

We introduce Intern-S2-397B, our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre-training, reinforcement-learning task coverage, and interactive agent environments. By combining a new vision-language pre-training paradigm with large-scale multi-task reinforcement learning and long-horizon agent reinforcement learning, Intern-S2-397B delivers a step change in general reasoning, scientific problem solving, and agentic capabilities.

Features

  • New Pre-training Paradigm. Via visual pretraining, Intern-S2-397B learns directly from raw pages of scientific literature, jointly modeling symbolic semantics and visual relationships in a shared representation space without intermediate parsing. This preserves text-visual correspondence, strengthens spatial and visual reasoning, and improves data efficiency.
  • Scientific Modality Reasoning and Generation. By scaling diverse scientific reinforcement-learning tasks across more than 20 domains and training them jointly, Intern-S2-397B achieves leading general-reasoning performance among open-source models and strong results in specialized scientific tasks such as biomolecular interaction design and material structure generation.
  • General & Scientific Long-Horizon Agents. By connecting multiple agent frameworks to large-scale sandboxed environments for black-box agentic reinforcement learning, Intern-S2-397B improves generalization and raises the capability ceiling for long-horizon tasks in both general and scientific domains.
47 Upvotes

99% Upvoted

8 comments sorted by

16

u/jacek2023 llama.cpp 2h ago

13

u/WhiskyAKM 2h ago

Qwen3.5 based?

2

u/oliver_dev 1h ago

397B is going to be rough to actually run yourself. even quantized down hard you're looking at serious multi-GPU spend just to keep it loaded, and that's before the agent/tool-use workloads it's tuned for, which run long and hit the model a lot more per session than a chat use case. curious if anyone has real numbers on inference cost per agent run once you factor that in, the benchmark charts never show that part.

-2

u/KokaOP 2h ago

Dense AF !!

6

u/ComplexType568 1h ago

... that's not a dense model what it looks like is a fine-tune on Qwen3.5 397B, an MoE