ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools GLM-4: - closely rivals GPT-4 on MMLU, MATH, GPQA, etc - gets close to GPT-4 in instruction following and long context tasks hf: huggingface.co/THUDM repo: github.com/THUDM abs:

ChatGLM A Family of Large Language Models from GLM-130B to GLM-4 All Tools We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air,

🚀 We published a tech report about GLM's Family! ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools. arxiv.org/html/2406.1279…

🏆Proud moment for us! Our paper on 'Explicit factor models for explainable recommendation'(u6v.cn/5OxPGm) has won the Test of Time Award at #SIGIR2024, leading the way in 'explainable recommendation' since 2014. Congrats to outstanding THUIR group from #DCST, #Tsinghua

We are not just doing “demo only” for video generation. Ying, we are bringing a video generation AI that everyone can use. Create a 6-second video in just 30 seconds. Try our new product now. YING:chatglm.cn/video medium.com/@ChatGLM/zhipu…

LongWriter Unleashing 10,000+ Word Generation from Long Context LLMs discuss: huggingface.co/papers/2408.07… Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words.

Thanks AK! We find that your long context LLM is secretly a LongWriter💡All you need is data with extended output during model alignment to unlock this capability. Our code, data, and models: github.com/THUDM/LongWrit…

#VisualAgentBench: proprietary models (4o, 4o-mini, 3.5-sonnet) currently have an edge as visual foundation agents for now, but open models InternVL & GLM-4V are catching up fast, a similar story to LLMs as agents as revealed in #AgentBench back in Aug 2023.

New from Tsinghua KEG (THUDM) LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs author Yushi Bai@ACL 2025 is active in discussion section to answer your questions: huggingface.co/papers/2408.07…

LongWriter-glm4-9b from Tsinghua KEG (THUDM) is capable of generating 10,000+ words at once!🚀 Paper identifies a problem with current long context LLMs -- they can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding lengths of 2,000 words. Paper proposes that an

What has just happened? Tsinghua KEG (THUDM) has just released the CogVideoX image-to-video generation model. Amazing result. Combined demo of T2V/I2V and V2V: huggingface.co/spaces/THUDM/C… Please duplicate the space with a L4s to avoid the long waiting queue. Model: huggingface.co/THUDM/CogVideo…

Thank you to the passionate developers for your continued support and patience. CogVideoX-5B-I2V, release!😀 Github: github.com/THUDM/CogVideo CogVideoX-5B-I2V model: huggingface.co/THUDM/CogVideo… Gradio space: huggingface.co/spaces/THUDM/C…

#AutoGLM: Autonomous Foundation Agents for GUIs by Xiao Liu (Shaw) and team at Tsinghua KEG (THUDM) & ChatGLM! Here are some AutoGLM for phone use demos --- beta testing since Oct 25 --- and its tech report arxiv.org/abs/2411.00820

🌈AndroidLab: a comprehensive platform for developing and evaluating Android agents. By integrating a controlled environment and standardized benchmarks, and leveraging the Android Instruct dataset, we significantly boost open-source model performance. github.com/THUDM/Android-…

AI has a "last-mile problem" similar to self-driving cars. With self-driving cars, early demos impressed, but real-world deployment took years. It's easy to hack up a prototype, but making it work reliably at scale is hard. If each step of an AI agent is only 95% accurate,

Ruslan Salakhutdinov at the Adaptive Foundation Models Workshop!

Check out our latest blog post about MiniVLA, a smaller open-source vision-language-action model! ai.stanford.edu/blog/minivla/

🏆Congrats to the Storage Research Group from #Tsinghua DCST for winning the#ASPLOS2025/#EuroSys2025 Large-Scale Model Inference Optimization Contest in Rotterdam! They outperformed global competitors, boosting inference performance by 1.1x using AWS NKI framework optimizations.

Prof. Liu's team built an #AI doctor for everyday #healthcare! In a #virtual hospital, it treated 10K+ virtual patients with 93% accuracy. They covered 300+ diseases across 21 departments & released BioMedGPT, PathOrchestra, and more for a full #medical AI pipeline. #THUAndBeyond