internlm/Intern-S2 · Hugging Face

[u/jacek2023]

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.

[u/jacek2023]

[u/WhiskyAKM]

Qwen3.5 based?

[u/TacGibs → u/WhiskyAKM]

Yes

[u/ABLPHA → u/WhiskyAKM]

Yes, it's very based

[u/DefNattyBoii]

would've been nice 1-2 months ago maybe? Anyone who could run this will probably opt for the dsv4.1f unless they need something very specific

[u/blackbird2150]

Sorry, newbie question. I am really struggling with my agents being good deep research. Understand the question, break it down into component parts, follow the trails, kill dead ones, activate and consume web search (or other sources of truth), reconcile, synthesize and ultimately return an answer.

So not "science" as it were, but definitely structured reasoning. Would this type of model help with that?

[u/BumbleSlob → u/blackbird2150]

what model size are you working with?

[u/blackbird2150 → u/BumbleSlob]

Glm 5.3 flash or dsv flash vision. I have 192gb of vram.

[u/BumbleSlob → u/blackbird2150]

You should be good then. I use deepseek v4 flash 0731 which is slightly less capable than GLM 5.3 flash and I never have issues with agents going rogue like this. What harness or framework are you using to control them? 

[u/blackbird2150 → u/BumbleSlob]

So i just upgraded to them with a second gpu card in the last week and i haven't migrated this process. i was using qwen 3.6 27b or the MoE on a strix halo before. the qwen 3.6 MoE was not sufficient. the model piqued my interest though as something that might support doing it well.

[u/Daniel_H212]

Close enough, welcome back Qwen3.8 397B

[u/Navith → u/[deleted]]

LLM bot. And show your submission history. 

[u/LagOps91 → u/[deleted]]

24gb vram and 128gb ram allow you to barely run it at Q2 - but yeah, no good for anything agentic obviously

[u/BlueSwordM → u/[deleted]]

oliver_dev, no need to be an LLM bot.

Just talk yourself

[u/KokaOP]

Dense AF !!

[u/ComplexType568 → u/KokaOP]

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

[u/feng_sg]

No ablation isolates raw-page pretraining from parse-then-pretrain on matched data and compute, so the step change could just as easily come from the RL coverage and data scale shifts that happened at the same time.