Intern Large Models

🔥Introducing #DataChef: an AI4AI framework that leverages reinforcement learning to automatically generate optimal data recipes for LLM adaptation. 🥳By exploring vast code spaces with an efficient proxy reward system, DataChef-32B matches the performance of top-tier models like Gemini-3-Pro in recipe generation, and its resulting recipes surpass industry-level expert curation on challenging benchmarks such as AIME'25 and ClimaQA. 🤗GitHub: github.com/yichengchen24/… 🤗Model:@HuggingModels huggingface.co/yichengchen24/… 🤗Demo: huggingface.co/spaces/yicheng…

🔥Introducing ARM-Thinker, the first Agentic multimodal Reward Model that autonomously invokes external tools to ground judgments in verifiable evidence. Accepted to CVPR 2026! 🥳Integrates three families of multimodal tools: 1⃣Image Crop & Zoom-in for fine-grained visual inspection. 2⃣Document Retrieval for multi-page evidence gathering. 3⃣Instruction-Following Validators for constraint verification. 🥳With a Think-Act-Verify loop, ARM-Thinker can call image crop & zoom-in, document retrieval, and instruction-following validators for evidence-based evaluation. 🥳Built on Qwen2.5-VL-7B with SFT + two-stage GRPO, ARM-Thinker improves multimodal reward modeling, tool-use reasoning, and multimodal math/logical reasoning. 😉+16.2% on reward modeling benchmarks (outperforming GPT-4o). 😉+9.6% on tool-use / think-with-images tasks (matching Mini-o3). 😉+4.2% on multimodal math & logical reasoning. 🥳Also introduce ARMBench-VL, the first multimodal reward benchmark that requires tool use. 📄 Paper: arxiv.org/abs/2512.05111 💻 Code: github.com/InternLM/ARM-T… 🤗 Dataset: @huggingface huggingface.co/datasets/inter… 🧪 Evaluation: github.com/open-compass/V…

🚀Meet InternVL-U: a lightweight 4B unified multimodal model that brings reasoning, generation, and editing into a unified framework. 🔥Built upon unified contextual modeling, modality-specific modular design, and decoupled visual representations, InternVL-U achieves a strong performance-efficiency trade-off, consistently outperforming unified baselines with over 3× larger model scales on challenging tasks such as text rendering, scientific reasoning, and spatially grounded generation and editing. 😉Open-source and designed for efficient, practical multimodal intelligence. 🤗GitHub: github.com/OpenGVLab/Inte… 🤗Hugging Face: @huggingface huggingface.co/InternVL-U/Int… 🤗GenEditEvalKit: github.com/open-compass/G… 🤗TextEdit: github.com/open-compass/T… 🤗Tech report: arxiv.org/pdf/2603.09877

@JustinLin610 🫡Thank you for all your contributions to the open-source community.

me stepping down. bye my beloved qwen.

@sunnypause internlm.intern-ai.org.cn/api/document?l…

@intern_lm where api? how to try? openrouter? anthropic endpoint? thanks!

🚀Introducing Intern-S1-Pro, an advanced 1T MoE open-source multimodal scientific reasoning model. 1⃣SOTA scientific reasoning, competitive with leading closed-source models across AI4Science tasks. 2⃣Top-tier performance on advanced reasoning benchmarks, strong general multimodal performance on various benchmarks. 3⃣1T-A22B MoE training efficiency with STE routing (dense gradient for router training) and grouped routing for stable convergence and balanced expert parallelism. 4⃣Fourier Position Encoding (FoPE) + upgraded time-series modeling for better physical signal representation; supports long, heterogeneous time-series (10^0–10^6 points). 😍Intern-S1-Pro is now supported by vLLM @vllm_project and SGLang @sgl_project @lmsysorg — more ecosystem integrations are on the way. ☺️Model：@huggingface huggingface.co/internlm/Inter… ☺️GitHub: github.com/InternLM/Inter… ☺️Try it now at: chat.intern-ai.org.cn

🚀 Meet Intern-S1-Pro: A massive 1T parameter MoE model for Multimodal Science Reasoning! ✅ 512 Experts (22B active) ✅ SOTA in AI4Science (Chemistry, Materials, Bio) ✅ FoPE + Time-series modeling (up to 10⁶ points) ✅ Native "Thinking Mode" support Open-source science just leveled up. 🧪💻 Model: modelscope.cn/models/Shangha… Github: github.com/InternLM/Inter…

🎉 Congrats to @intern_lm on Intern-S1-Pro — day-0 support in vLLM! 🔬 Trillion-scale MoE for scientific reasoning: 1T total params, 512 experts, 22B activated per token. State-of-the-art across AI4Science domains. PR: github.com/vllm-project/v… Serving command (✅ Verified on NVIDIA GPUs):

😊 Congrats to @intern_lm on releasing Intern-S1-Pro, a 1T-parameter MoE multimodal scientific reasoning model. Day-0 support is now live in SGLang! Highlights: 📖 SOTA AI4Science reasoning, competitive with top closed models; strong advanced reasoning + multimodal benchmarks ⚙️ 1T-A22B MoE with STE & grouped routing; FoPE + long time-series modeling Related PR: github.com/sgl-project/sg… Try it with the following command:

☺️Models on ModelScope. modelscope.cn/models/Shangha… @ModelScope2022

🚀 Introducing Spatial-SSRL, the first study which proposes a Self-Supervised Reinforcement Learning paradigm for spatial understanding. 💡 Spatial-SSRL a lightweight tool-free framework that aims at enhancing spatial intelligence and is natually compatible with the RLVR training paradigm. Only raw 2D and RGB-D images are required and we avoid any use of human annotation, external proprietary model or expert model throughout the entire pipeline, making Spatial-SSRL highly cost-effective and scalable. 🛰️ Spatial-SSRL comprises five pretext tasks now: shuffled patch reordering, flipped patch recognition, cropped patch inpainting, regional depth ordering, and 3D relative position prediction. Thanks to its lightweight characteristics, Spatial-SSRL can be easily extended to more pretext tasks and we welcome the whole community to join Spatial-SSRL with effective pretext tasks! 🤖 After applying Spatial-SSRL, we significantly enhance the performance of spatial understanding on Qwen2.5-VL (3B&7B) and Qwen3-VL (4B), as well as retaining their general visual capabilities. 🤗 Currently, we have released the repository of Spatial-SSRL, the dataset Spatial-SSRL-81k, and the trained models: Spatial-SSRL-7B and Spatial-SSRL-Qwen3VL-4B. The total download of the models and dataset has surpassed 1,000. 👇 Try Spatial-SSRL-7B now at: huggingface.co/spaces/yuhangz… Paper: arxiv.org/abs/2510.27606 Github: github.com/InternLM/Spati… Model (on Qwen2.5-VL): huggingface.co/internlm/Spati… Model (on Qwen3-VL): huggingface.co/internlm/Spati… Dataset: huggingface.co/datasets/inter…

🚀Introducing #CapRL, the first study of applying GRPO for the open-ended and subjective image captioning task. 🤯 🤖The trained CapRL-3B model achieves image captioning performance comparable to Qwen2.5-VL-72B. ✨CapRL introduces a novel training framework that redefines caption quality through its utility: a high-quality caption should enable a non-visual language model to accurately answer questions about the corresponding image. 📈Currently, CapRL is open-sourced, with total downloads of the models and datasets surpassing 7,000. The research team is continuously iterating with stronger base models and improved training recipe. 👇 Try it now at: huggingface.co/spaces/yuhangz… Paper: arxiv.org/abs/2509.22647 GitHub: github.com/InternLM/CapRL Model: huggingface.co/internlm/CapRL… Dataset: huggingface.co/datasets/inter…

🚀 Big news for #lmdeploy v0.10.1! 🥳Our #FP8 high-performance inference is no longer limited to the latest #GPUs. It now supports all #NVIDIA architectures from V100 onwards, bringing major speedups to more users. 🤗github.com/InternLM/lmdep…

Introducing #DLCompiler and #DLBlas. Developers can achieve performance close to the hardware peak without manual tuning. And for the first time, Triton OP achieves extreme performance optimization on DSA chips. DLCompiler: github.com/DeepLink-org/D… DLBlas：github.com/DeepLink-org/D…

🔥LMDeploy v0.10.0 released! 😊Supercharges OpenAI’s GPT-OSS MXFP4 models. 😊Delivers exceptional performance for GPT-OSS models on V100 and higher GPUs. 😊On H800 & A100, LMDeploy outperforms vLLM across all scenarios—faster, more efficient inference! 🤗github.com/InternLM/lmdep…

🔥Introducing Intern-S1-mini, a lightweight open-source multimodal reasoning model based on the same techniques as Intern-S1. 🥳With just 8B parameters, it’s optimized for fast deployment and easy customization. - Strong general capabilities while excelling in specialized scientific domains. - Built upon an 8B dense language model and a 0.3B vision encoder. - A capable research assistant for real-world scientific applications. 🤗Model：@huggingface huggingface.co/internlm/Inter… 🤗GitHub: github.com/InternLM/Inter… 🤗Try it now at: chat.intern-ai.org.cn #InternS1

🔥🔥🔥 😊Try our #InternS1😋 😉Model: huggingface.co/internlm/Inter… 😉GitHub: github.com/InternLM/Inter… 😉Chat here: chat.intern-ai.org.cn

Our paper won an outstanding paper on ACL 2025. Try our best open-source multimodal reasoning model Intern-S1 at huggingface.co/internlm/Inter…. This 241B MoE model combines strong general-task capabilities with state-of-the-art performance on a wide range of scientific tasks, rivaling leading closed-source commercial models.

Try our new multimodal reasoning model #InternS1😋 Model: huggingface.co/internlm/Inter… GitHub: github.com/InternLM/Inter… Chat here: chat.intern-ai.org.cn

🚀Introducing Intern-S1, our most advanced open-source multimodal reasoning model yet! 🥳Strong general-task capabilities + SOTA performance on scientific tasks, rivaling leading closed-source commercial models. 🥰Built upon a 235B MoE language model and a 6B Vision encoder. 🥰Pretrained on 5T tokens (50%+ scientific data). 🥳Dynamic tokenizer enables native understanding of molecular formulas, protein sequences, and seismic signals. 🤗Model：@huggingface huggingface.co/internlm/Inter… 🤗GitHub: github.com/InternLM/Inter… Try it now at: 🤗chat.intern-ai.org.cn