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Top 23 Jupyter Notebook Transformer Projects
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View on GitHub
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SaaSHub
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Transformers-Tutorials
This repository contains demos I made with the Transformers library by HuggingFace.
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VAR
[NeurIPS 2024 Best Paper Award][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation! (by FoundationVision)
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Chinese-CLIP
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
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pytorch-sentiment-analysis
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
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transformers-interpret
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
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Transformer-MM-Explainability
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
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Project mention: DeepSeek-V4-Flash means LLM steering is interesting again | news.ycombinator.com | 2026-05-16
This reminds me of control vectors, especially this line in the linked DwarfStar repo:
> y = y - scale * direction[layer] * dot(direction[layer], y)
From https://vgel.me/posts/representation-engineering/
> A control vector is a vector (technically a list of vectors, one per layer) that you can apply to model activations during inference to control the model's behavior without additional prompting
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You can pretty much use it as a drop-in replacement for anything built on top of DINOv2. E.g. if you want to fine-tune a segmentation model you can use EoMT[0] which uses DINOv2 as backbone and replace the backbone with DINOv3. If you just want to run it you can give LightlyTrain a spin [1]. There should also be support in the original EoMT repo soon. The methods in the DINOv3 paper focus on frozen backbones which are usually faster to train but might have lower performance than full fine-tuning.
[0]: https://github.com/tue-mps/eomt
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language-planner
Official Code for "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents"
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diffusers-interpret
Diffusers-Interpret 🤗🧨🕵️♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.
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easy_ViTPose
Easy and fast 2d human and animal multi pose estimation using SOTA ViTPose [Y. Xu et al., 2022] Real-time performances and multiple skeletons supported.
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REaLTabFormer
A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.
Jupyter Notebook Transformers discussion
Jupyter Notebook Transformers related posts
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DeepSeek-V4-Flash means LLM steering is interesting again
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Representation Engineering
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Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer
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OpenChat 3.2 SUPER is Here!
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Refact LLM: New 1.6B code model reaches 32% HumanEval and is SOTA for the size
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OpenChat: Advancing Open-Source Language Models with Imperfect Data
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Creating a new Finetuned model
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A note from our sponsor - SaaSHub
www.saashub.com | 12 Jul 2026
Index
What are some of the best open-source Transformer projects in Jupyter Notebook? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | generative-ai-for-beginners | 112,851 |
| 2 | LLMs-from-scratch | 98,779 |
| 3 | Transformers-Tutorials | 11,672 |
| 4 | VAR | 8,705 |
| 5 | Chinese-CLIP | 5,964 |
| 6 | pytorch-sentiment-analysis | 4,608 |
| 7 | mup | 1,697 |
| 8 | uni2ts | 1,542 |
| 9 | transformers-interpret | 1,412 |
| 10 | Transformer-MM-Explainability | 908 |
| 11 | MachineLearning-QandAI-book | 910 |
| 12 | course-content-dl | 810 |
| 13 | repeng | 742 |
| 14 | eomt | 611 |
| 15 | optimum-intel | 603 |
| 16 | gpt2bot | 442 |
| 17 | browser-ml-inference | 316 |
| 18 | language-planner | 281 |
| 19 | diffusers-interpret | 278 |
| 20 | neuralgraffiti | 275 |
| 21 | vision-transformer-from-scratch | 251 |
| 22 | easy_ViTPose | 248 |
| 23 | REaLTabFormer | 245 |