InternLM3-8B-instruct🔥 Trained on just 4T tokens, it outperforms Llama3.1-8B and Qwen2.5-7B in reasoning tasks, at 75% lower cost! internlm/internlm3-67875827c377690c01a9131d
🖖 Let me introduce the work I've done over the past three months: 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕 and 𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕-𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁, now open-sourced on 🤗 Hugging Face.
𝗹𝗶𝗮𝗻𝗴𝗵𝘀𝘂𝗻/𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕: This model is built on top of 𝗺𝗲𝘁𝗮-𝗹𝗹𝗮𝗺𝗮/𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝟯𝗕 with continual pretraining. The training dataset consists of a mixture of Traditional Chinese and multilingual texts in specific proportions, including 20B tokens of Traditional Chinese text.
𝗹𝗶𝗮𝗻𝗴𝗵𝘀𝘂𝗻/𝗟𝗹𝗮𝗺𝗮-𝟯.𝟮-𝗧𝗮𝗶𝘄𝗮𝗻-𝟯𝗕-𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁: This is a fine-tuned conversational model based on the foundation model.
This Llama-3.2-Taiwan open-source project is currently a one-person effort (yes, I did everything from text preparation — so exhausting!). If you're interested, feel free to join the Discord server for discussions.
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The evaluation was conducted using ikala/tmmluplus, though the README page does not yet reflect the latest results. The performance is close to the previous versions, indicating that further improvements might require adding more specialized knowledge in the datasets.
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If anyone is willing to provide compute resources, it would be greatly appreciated to help this project continue and grow. 💪
✨ MiniMax-text-01: - 456B with 45.9B activated per token - Combines Lightning Attention, Softmax Attention, and MoE for optimal performance - Training context up to 1M tokens, inference handles 4M tokens
✨ MiniMax-VL-01: - ViT-MLP-LLM framework ( non-transformer👀) - Handles image inputs from 336×336 to 2016×2016 - 694M image-caption pairs + 512B tokens processed across 4 stages
MiniCPM-o2.6 🔥 an end-side multimodal LLMs released by OpenBMB from the Chinese community Model: openbmb/MiniCPM-o-2_6 ✨ Real-time English/Chinese conversation, emotion control and ASR/STT ✨ Real-time video/audio understanding ✨ Processes up to 1.8M pixels, leads OCRBench & supports 30+ languages
🙋🏻♂️Hey there folks , Open LLM Europe just released Lucie 7B-Instruct model , a billingual instruct model trained on open data ! You can check out my unofficial demo here while we wait for the official inference api from the group : Tonic/Lucie-7B hope you like it 🚀
🔥 The AI Agent hype is real! This blog post deep dives into everything you need to know before deploying them: from key definitions to practical recommendations. A must-read for anyone building the future of autonomous systems.
📊 Key insight: A clear table breaking down the 5 levels of AI agents - from simple processors to fully autonomous systems. Essential framework for understanding where your agent stands on the autonomy spectrum
⚖️ Deep analysis of 15 core values reveals critical trade-offs: accuracy, privacy, safety, equity & more. The same features that make agents powerful can make them risky. Understanding these trade-offs is crucial for responsible deployment
🎯 6 key recommendations for the road ahead: - Create rigorous evaluation protocols - Study societal effects - Understand ripple effects - Improve transparency - Open source can make a positive difference - Monitor base model evolution
Hey everyone 🤗! Check out this new Virtual Try Off model (based on SD1.5): 1aurent/TryOffAnyone This model isn't as accurate as others (e.g. xiaozaa/cat-try-off-flux based on FLUX.1) but it sure is fast!
QvQ-72B-Preview🎄 an open weight model for visual reasoning just released by Alibaba_Qwen team Qwen/qvq-676448c820912236342b9888 ✨ Combines visual understanding & language reasoning. ✨ Scores 70.3 on MMMU ✨ Outperforms Qwen2-VL-72B-Instruct in complex problem-solving
🔍 From instruction-following to creative storytelling, dive into 2024's most impactful AI datasets! These gems are shaping everything from scientific research to video understanding.
Did a fun experiment: What are the main themes emerging from the 100+ Nieman Journalism Lab predictions for 2025?
I used natural language processing to cluster and map them — really helps spot patterns that weren't obvious when reading predictions one by one. So what will shape journalism next year? A lot of AI and US politics (surprise!), but there's also this horizontal axis that spans from industry strategies to deep reflections on how to talk to the public.
Click any dot to explore the original prediction. What themes surprise/interest you the most?
Megrez-3B-Omni 🔥 an on-device multimodal LLM by Infinigence AI, another startup emerging from the Tsinghua University ecosystem. Model: Infinigence/Megrez-3B-Omni Demo: Infinigence/Megrez-3B-Omni ✨Supports analysis of image, text, and audio modalities ✨Leads in bilingual speech ( English & Chinese ) input, multi-turn conversations, and voice-based queries ✨Outperforms in scene understanding and OCR across major benchmarks
