gyunggyung
commented
1 year ago LLaMA 이야기.
최근 페이스북이 공유한 LLaMA의 더 최신 이야기 입니다.
- LLaMA-7B: 체크포인드를 공개해주셨습니다. 어떻게 만드신 건지 아직 모르겠습니다.
- llama-up-data: hunkim님께서 LLaMA로 챗봇을 만들어주셨습니다. 다만 작다 보니 성능은....
- llama-int8: 양자화를 잘 해서 3090, 4090에서도 돌아갈 수 있게 만들어주셨습니다. LLaMA INT8 Inference guide
- LLaMA 질문: 궁금한 질문을 했지만 누구도 답을 주지 않는....
이미지 관련 모델.
- Beating OpenAI CLIP with 100x less data and compute: 100배 적은 데이터로도 좋은 성능을 보여줍니다. 범용성이 높아서 추후에 많이 쓰지 않을까 싶습니다. 심지어 한국어도 잘합니다. 관련해서 질문이 있으시면 제가 대신 전달 드릴 수도 있습니다.
- AI Generated Images Are Getting Too Real | Asmongold Reacts: 이제 이미지 생성을 정말 자연스럽게 합니다. 특히 LoRA는 공부를 해봐야겠습니다. 관련 결과를 몇 개 공유합니다.
- AI Art is getting too good! Can YOU Tell the Difference?: 인공지능이 그린 것을 찾아보세요!
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Must read: the 100 most cited AI papers in 2022
2022
1️⃣ AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models -> (From DeepMind, 1372 citations) Using AlphaFold to augment protein structure database coverage.
2️⃣ ColabFold: making protein folding accessible to all -> (From multiple institutions, 1162 citations) An open-source and efficient protein folding model.
3️⃣ Hierarchical Text-Conditional Image Generation with CLIP Latents -> (From OpenAI, 718 citations) DALL·E 2, complex prompted image generation that left most in awe.
4️⃣ A ConvNet for the 2020s -> (From Meta and UC Berkeley, 690 citations) A successful modernization of CNNs at a time of boom for Transformers in Computer Vision.
5️⃣ PaLM: Scaling Language Modeling with Pathways -> (From Google, 452 citations) Google's mammoth 540B Large Language Model, a new MLOps infrastructure, and how it performs.
2021 1️⃣ Highly accurate protein structure prediction with AlphaFold -> (From DeepMind, 8965) AlphaFold, a breakthrough in protein structure prediction using Deep Learning.
2️⃣ Swin Transformer: Hierarchical Vision Transformer using Shifted Windows -> (From Microsoft, 4810 citations) A robust variant of Transformers for Vision.
3️⃣ Learning Transferable Visual Models From Natural Language Supervision -> (From OpenAI, 3204 citations) CLIP, image-text pairs at scale to learn joint image-text representations in a self supervised fashion
4️⃣ On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? -> (From U. Washington, Black in AI, The Aether, 1266 citations) Famous position paper very critical of the trend of ever-growing language models, highlighting their limitations and dangers.
5️⃣ Emerging Properties in Self-Supervised Vision Transformers -> (From Meta, 1219 citations) DINO, showing how self-supervision on images led to the emergence of some sort of proto-object segmentation in Transformers.
2020 1️⃣ An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale -> (From Google, 11914 citations) The first work showing how a plain Transformer could do great in Computer Vision.
2️⃣ Language Models are Few-Shot Learners -> (From OpenAI, 8070 citations) GPT-3, This paper does not need further explanation at this stage.
3️⃣ YOLOv4: Optimal Speed and Accuracy of Object Detection -> (From Academia Sinica, Taiwan, 8014 citations) Robust and fast object detection sells like hotcakes.
4️⃣ Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer -> (From Google, 5906 citations) A rigorous study of transfer learning with Transformers, resulting in the famous T5.
5️⃣ Bootstrap your own latent: A new approach to self-supervised Learning -> (From DeepMind and Imperial College, 2873 citations) Showing that negatives are not even necessary for representation learning.
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