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Top 23 Python Transformer Projects
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While its tough to say something specifc since we dont know how exactly you trained it or the prompt format of your training input or how you are performing inference, one thing I found when I faced similar types of issues is that the model does not know when to stop. Some of it is because the fast llama tokenizer does not add the token when encoding your inputs. So you can either add that token explicitly in your input text for each sample or use the slow llama tokenizer. Check llama_recipes github repo for the exact issue https://github.com/huggingface/transformers/issues/22794. The other most probable thing you might want to check is if the model.generate output contains the exact input tokens too. That is the expected behavior of some models (like llama2 or mpt) for example when you use vanilla transformers for inference.
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When I look for benchmarks I always start here https://paperswithcode.com/task/instance-segmentation/codeless it has the lists of datasets to measure models accross lots o papers. Many are very specific models with low support or community but it gives you a good idea of the state of the art. It also lists repositories related to good community. https://github.com/open-mmlab/mmdetection seems very active and the one that is being used the most, you could use the models that it has integrated in its model zoo, within the same repository. It has the benchmarks to compare those same models and some of them are from 2022
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
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For Python, here's a nice compilation: https://github.com/ml-tooling/best-of-ml-python/blob/main/RE...
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Heh, so it totally depends on the use case.
I use GPT4 constantly to chat through issues I am working on and get different perspectives. I cannot do that with local models.
On the other hand, I have been processing a ton of text transcripts with a fine tuned llama2 13b model i've been working on, and for the tasks I have fine-tuned on, my local model is producing better results than GPT4, often taking a task that I had to do in multiple steps with GPT4, and being able to complete it in a single shot.
I can run my local model through vLLM on my workstation at around the same tokens/sec as I can spend maxing out my API limits with GPT3.5-turbo (~$20/hr) while running on 2x 3090's. I'm hitting the vLLM (OpenAI clone) chat/completions endpoint. My model implements the HF chat_templates feature, and I worked on adding support for that to vLLM: https://github.com/vllm-project/vllm/pull/1756 (llama.cpp is talking about adding support for it too) so I could easily swap out my model in my data pipeline in place of GPT3.5/GPT4, and I wouldn't have to keep maintaining that code on my side.
So, with these transcripts I've been:
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RWKV-LM
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
That is not true. There are RNNs with transformer/LLM-like performance. See https://github.com/BlinkDL/RWKV-LM.
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PaddleSpeech
Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.
PaddlePaddle/PaddleSpeech
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and the implementation https://github.com/google/trax/blob/master/trax/models/resea... if you are interested.
Hope you get to look into this!
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Onboard AI
Learn any GitHub repo in 59 seconds. Onboard AI learns any GitHub repo in minutes and lets you chat with it to locate functionality, understand different parts, and generate new code. Use it for free at www.getonboard.dev.
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PaddleSeg
Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
Project mention: [Medical Segmentation] The all-in-one 3D medical image segmentation toolkit. From data annotation to model deployment, you are welcome to try it all！ | /r/ArtificialInteligence | 2022-12-19EISeg-Med3D: https://github.com/PaddlePaddle/PaddleSeg/blob/develop/EISeg/med3d/README_en.md
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LMFlow
An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
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openai/jukebox: Music Generation
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Project mention: [D] The MMSegmentation library from OpenMMLab appears to return the wrong results when computing basic image segmentation metrics such as the Jaccard index (IoU - intersection-over-union). It appears to compute recall (sensitivity) instead of IoU, which artificially inflates the performance metrics. | /r/MachineLearning | 2023-03-06
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Project mention: Show HN: Fully client-side GPT2 prediction visualizer | news.ycombinator.com | 2023-09-05
It would be interesting to have attention visualized as well, similar to how it's done in BertViz:
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Project mention: Distil-Whisper: distilled version of Whisper that is 6 times faster, 49% smaller | news.ycombinator.com | 2023-10-31
That's the implication. If the distil models are same format as original openai models then the Distil models can be converted for faster-whisper use as per the conversion instructions on https://github.com/guillaumekln/faster-whisper/
So then we'll see whether we get the 6x model speedup on top of the stated 4x faster-whisper code speedup.
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Project mention: Certain directories (e.g. SwinIR) are empty (version: Empire Media Science A1111 Web UI Installer) | /r/StableDiffusion | 2023-03-17
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Efficient-AI-Backbones
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
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Project mention: [DISC] - The angel who came to pick me up is a Gal (Oneshot by Shiraishi Kouhei) | /r/manga | 2023-09-06
OCR works pretty good. ocr.space, ocr.best and cotrans.touhou.ai/ are all pretty nice.
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HRNet-Semantic-Segmentation
The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919
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towhee
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2023-12-01.
Python Transformer related posts
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- Open Source Libraries
- A look at Apple’s new Transformer-powered predictive text model
- OpenAI compatible API for LLMs (and embeddings) with ready-to-use Docker images
- [D] What is the most efficient version of OpenAI Whisper?
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Index
What are some of the best open-source Transformer projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | transformers | 116,187 |
| 2 | mmdetection | 26,197 |
| 3 | best-of-ml-python | 14,691 |
| 4 | vllm | 10,443 |
| 5 | RWKV-LM | 10,351 |
| 6 | PaddleSpeech | 9,156 |
| 7 | trax | 7,809 |
| 8 | PaddleSeg | 7,803 |
| 9 | LaTeX-OCR | 7,666 |
| 10 | LMFlow | 7,467 |
| 11 | jukebox | 7,297 |
| 12 | GPT2-Chinese | 7,216 |
| 13 | mmsegmentation | 6,773 |
| 14 | bertviz | 5,930 |
| 15 | faster-whisper | 5,814 |
| 16 | BERT-pytorch | 5,785 |
| 17 | Informer2020 | 4,401 |
| 18 | OpenPrompt | 3,931 |
| 19 | SwinIR | 3,759 |
| 20 | Efficient-AI-Backbones | 3,584 |
| 21 | manga-image-translator | 2,983 |
| 22 | HRNet-Semantic-Segmentation | 2,976 |
| 23 | towhee | 2,853 |
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