promptsource
transformers
promptsource | transformers | |
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11 | 176 | |
2,505 | 125,369 | |
2.2% | 1.7% | |
4.6 | 10.0 | |
6 months ago | 1 day ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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promptsource
- How to Prompt Design? Share resources
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Any tips for hiring prompt engineers?
Bigscience Promptsource
- PromptSource: Toolkit for creating, sharing and using natural language prompts
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Hugging Face Introduces “T0”, An Encoder-Decoder Model That Consumes Textual Inputs And Produces Target Responses
Quick 5 Min Read | Paper|Github
- 16x smaller than GPT3 but better [video]
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[R] BigScience's first paper, T0: Multitask Prompted Training Enables Zero-Shot Task Generalization
Code for https://arxiv.org/abs/2110.08207 found: https://github.com/bigscience-workshop/promptsource/
- "P3: Public Pool of Prompts" (BigScience's collaborative collection of >2k prompts for >170 datasets)
- BigScience's guide to using templating languages to develop prompts
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word2vec chatbot
I'd use a prompted dataset then, as well as explore the TO model framework.
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First model released by BigScience outperforms GPT-3 while being 16x smaller
We fine-tuned the model on a dozens of different NLP datasets and tasks in a prompted style. You can read all the prompts in the appendix or get them all here: https://github.com/bigscience-workshop/promptsource . Most NLP tasks are not particularly freeform, or they are naturally length limited like summary (XSum is very short). As a consequence, the model mostly defaults to short responses. Your "trick" is not that unreasonable though! Many of the training prompts that want long responses, ask for them explicitly.
transformers
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AI enthusiasm #9 - A multilingual chatbot📣🈸
transformers is a package by Hugging Face, that helps you interact with models on HF Hub (GitHub)
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Maxtext: A simple, performant and scalable Jax LLM
Is t5x an encoder/decoder architecture?
Some more general options.
The Flax ecosystem
https://github.com/google/flax?tab=readme-ov-file
or dm-haiku
https://github.com/google-deepmind/dm-haiku
were some of the best developed communities in the Jax AI field
Perhaps the “trax” repo? https://github.com/google/trax
Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...
Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py
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Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
The HuggingFace transformers library already has support for a similar method called prompt lookup decoding that uses the existing context to generate an ngram model: https://github.com/huggingface/transformers/issues/27722
I don't think it would be that hard to switch it out for a pretrained ngram model.
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AI enthusiasm #6 - Finetune any LLM you want💡
Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️
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Schedule-Free Learning – A New Way to Train
* Superconvergence + LR range finder + Fast AI's Ranger21 optimizer was the goto optimizer for CNNs, and worked fabulously well, but on transformers, the learning rate range finder sadi 1e-3 was the best, whilst 1e-5 was better. However, the 1 cycle learning rate stuck. https://github.com/huggingface/transformers/issues/16013
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Gemma doesn't suck anymore – 8 bug fixes
Thanks! :) I'm pushing them into transformers, pytorch-gemma and collabing with the Gemma team to resolve all the issues :)
The RoPE fix should already be in transformers 4.38.2: https://github.com/huggingface/transformers/pull/29285
My main PR for transformers which fixes most of the issues (some still left): https://github.com/huggingface/transformers/pull/29402
- HuggingFace Transformers: Qwen2
- HuggingFace Transformers Release v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2
- HuggingFace: Support for the Mixtral Moe
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Paris-Based Startup and OpenAI Competitor Mistral AI Valued at $2B
If you want to tinker with the architecture Hugging Face has a FOSS implementation in transformers: https://github.com/huggingface/transformers/blob/main/src/tr...
If you want to reproduce the training pipeline, you couldn't do that even if you wanted to because you don't have access to thousands of A100s.
What are some alternatives?
eai-prompt-gallery - Library of interesting prompt generations
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
natural-instructions - Expanding natural instructions
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
llama - Inference code for Llama models
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
rasa - 💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
huggingface_hub - The official Python client for the Huggingface Hub.
OpenNMT-py - Open Source Neural Machine Translation and (Large) Language Models in PyTorch