DeepLearningExamples
alpaca_eval
| DeepLearningExamples | alpaca_eval | |
|---|---|---|
| 7 | 4 | |
| 12,642 | 1,115 | |
| 1.2% | 8.4% | |
| 6.1 | 9.6 | |
| about 1 month ago | 3 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | Apache License 2.0 |
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DeepLearningExamples
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A small example from Tacotron2 trained on Brandon "Atrioc" Ewing
GitHub Used: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2
- Retraining Single Shot MultiBox Detector model on a custom data set?
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Nvidia Scientists Take Top Spots in 2021 Brain Tumor Segmentation Challenge
Disclosure: I used to work on Google Cloud.
I dunno, their A100 results took about 20-30 minutes on 8 x A100s [1]. 8xA100s is like $24/hr on GCP at on-demand rates.
The efficiency was okay but not linear, so if you were more cost constrained you might go with 1xA100 for $3/hr and have ~2.5hr training times.
Getting that performance out of a GPU is more challenging than getting access to the GPUs. All the major cloud providers offer them.
(Nit: GCP deployed the 40 GiB cards rather than the later 80 GiB parts, but let's ignore that).
but it often doesn't matter
[1] https://github.com/NVIDIA/DeepLearningExamples/tree/master/P...
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Tacotron2 CPU Inferencing
Entrypoint.py file in tacotron2 folder: source code
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Skyrim Voice Synthesis Mega Tutorial
For those asking about differences to xVASynth, the models trained with xVASynth are the FastPitch models (https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/FastPitch). As a quick explainer:
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Modders develop AI based app for creating new voice lines using neural speech synthesis.
There's another separate tool set from Nvidia that's on GitHub that the creator used to train the models. I'm not going to pretend like I understand it, but you can find it here.
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[R] Data Movement Is All You Need: A Case Study on Optimizing Transformers
The Nvidia's implementation of BERT has a long way to go (I don't know about the implementations of input independent gradient computations in their backprop). But, there are scaled benchmarks on DGX A100's -https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT
alpaca_eval
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UltraLM-13B reaches top of AlpacaEval leaderboard
Alpaca Eval is open source and was developed by the same team who trained the alpaca model afaik. It is not like what you said in the other comment
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[P] AlpacaEval : An Automatic Evaluator for Instruction-following Language Models
I have been going deep in this space for my can-ai-code project and was looking at the config that WizardLM was run with: https://github.com/tatsu-lab/alpaca_eval/blob/main/src/alpaca_eval/models_configs/wizardlm-13b/configs.yaml
an automatic evaluator that is easy to use, fast, cheap and validated against 20K human annotations. It actually has a higher agreement with majority vote of humans than a single human annotator! Of course, our method still has limitations which we discuss here!
What are some alternatives?
lidar-harmonization - Code release for Intensity Harmonization for Airborne LiDAR
Local-LLM-Comparison-Colab-UI - Compare the performance of different LLM that can be deployed locally on consumer hardware. Run yourself with Colab WebUI.
Megatron-LM - Ongoing research training transformer models at scale
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
ontogpt - LLM-based ontological extraction tools, including SPIRES
llm-search - Querying local documents, powered by LLM
alpaca_farm - A simulation framework for RLHF and alternatives. Develop your RLHF method without collecting human data.
deep_navigation - Deep Learning based wall/corridor following P3AT robot (ROS, Tensorflow 2.0)
medmcqa - A large-scale (194k), Multiple-Choice Question Answering (MCQA) dataset designed to address realworld medical entrance exam questions.
notebooks - Notebooks illustrating the use of Norse, a library for deep-learning with spiking neural networks.
language-planner - Official Code for "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents"