I updated the LLM Scientist roadmap and added a ton of new information and references. It covers training, datasets, evaluation, quantization, and new trends like test-time compute scaling.
The LLM Course has been incredibly popular (41.3k stars!) and I've been touched to receive many, many messages about how it helped people in their careers.
I know how difficult this stuff can be, so I'm super proud of the impact it had. I want to keep updating it in 2025, especially with the LLM Engineer roadmap.
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
✨ 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 🚀