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This process allows the creation of two distinct datasets within Open-Critic-GPT:
Code-Preference-Pairs Dataset: (SFT) Contains pairs of duplicate code examples, with the only difference being one the rejected example has the bugged code 'surgically transplanted in' while the accepted is left the same.
Open-Critic-GPT Dataset: (DPO) Trains the model to find bugs and produce working code from broken code.
Both dataset's spans a total of 127 different language/structures, (some may have been lost in conversion started with 122k ended with 55k, due to lack of structured output, a finetuned model may preform better structured outputs.)
Both datasets contain of ~55K examples each (which both come from the same parent example)
Code-Preference-Pairs Dataset: (SFT) Contains pairs of duplicate code examples, with the only difference being one the rejected example has the bugged code 'surgically transplanted in' while the accepted is left the same.
Open-Critic-GPT Dataset: (DPO) Trains the model to find bugs and produce working code from broken code.
Both dataset's spans a total of 127 different language/structures, (some may have been lost in conversion started with 122k ended with 55k, due to lack of structured output, a finetuned model may preform better structured outputs.)
Both datasets contain of ~55K examples each (which both come from the same parent example)
Dataset Card for LLaVA-OneVision
!!! We are still uploading our dataset, stay tuned for final version, or contact [email protected] to get more details.
We provide the whole details of LLaVA-OneVision Dataset. In this dataset, we include the data splits used in the both final image stage and one-vision stage. For more details, please check our paper.
Dataset Sources
Dataset Collection: We include a few subsets from existing dataset collection Cambrian, Cauldron, UReader. Since we only used a few subsets from these datasets, and applied the cleaning and re-annotation process, we uploaded our processed version of these datasets into our own repository and thank the authors for providing the original datasets.
Other Datasets: For rest single source dataset, such as AI2D, OKVQA, we cite and link the original sources in our paper.
https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data
!!! We are still uploading our dataset, stay tuned for final version, or contact [email protected] to get more details.
We provide the whole details of LLaVA-OneVision Dataset. In this dataset, we include the data splits used in the both final image stage and one-vision stage. For more details, please check our paper.
Dataset Sources
Dataset Collection: We include a few subsets from existing dataset collection Cambrian, Cauldron, UReader. Since we only used a few subsets from these datasets, and applied the cleaning and re-annotation process, we uploaded our processed version of these datasets into our own repository and thank the authors for providing the original datasets.
Other Datasets: For rest single source dataset, such as AI2D, OKVQA, we cite and link the original sources in our paper.
https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data
huggingface.co
lmms-lab/LLaVA-OneVision-Data · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Dataset Card for magpie-ultra-v0.1
This dataset has been created with distilabel.
Dataset Summary
magpie-ultra it's a synthetically generated dataset for supervised fine-tuning using the new Llama 3.1 405B-Instruct model, together with other Llama models like Llama-Guard-3-8B and Meta-Llama-3.1-8B-Instruct.
The dataset contains challenging instructions and responses for a wide variety of tasks, such as Coding & debugging, Math, Data analysis, Creative Writing, advice seeking, or Brainstorming.
Explore the dataset in Argilla.
This dataset has been created with distilabel.
Dataset Summary
magpie-ultra it's a synthetically generated dataset for supervised fine-tuning using the new Llama 3.1 405B-Instruct model, together with other Llama models like Llama-Guard-3-8B and Meta-Llama-3.1-8B-Instruct.
The dataset contains challenging instructions and responses for a wide variety of tasks, such as Coding & debugging, Math, Data analysis, Creative Writing, advice seeking, or Brainstorming.
Explore the dataset in Argilla.
keras-io/wgan-molecular-graphs
Model description
This repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules WGAN-GP with R-GCN for the generation of small molecular graphs.
Full credits go to Alexander Kensert
Reproduced by Vu Minh Chien
Motivation: The development of new drugs (molecules) can be extremely time-consuming and costly. The use of deep learning models can alleviate the search for good candidate drugs, by predicting the properties of known molecules (e.g., solubility, toxicity, affinity to the target protein, etc.). As the number of possible molecules is astronomical, the space in which we search for/explore molecules is just a fraction of the entire space. Therefore, it's arguably desirable to implement generative models that can learn to generate novel molecules (which would otherwise have never been explored).
Model description
This repo contains the model and the notebook for implementing a generative model for graphs and using it to generate novel molecules WGAN-GP with R-GCN for the generation of small molecular graphs.
Full credits go to Alexander Kensert
Reproduced by Vu Minh Chien
Motivation: The development of new drugs (molecules) can be extremely time-consuming and costly. The use of deep learning models can alleviate the search for good candidate drugs, by predicting the properties of known molecules (e.g., solubility, toxicity, affinity to the target protein, etc.). As the number of possible molecules is astronomical, the space in which we search for/explore molecules is just a fraction of the entire space. Therefore, it's arguably desirable to implement generative models that can learn to generate novel molecules (which would otherwise have never been explored).
Butterfly GAN
Model description
Based on paper: Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
which states: "Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU, and has a consistent performance, even with less than 100 training samples"
also dubbed the Light-GAN model. This model was trained using the script here which is adapted from the lucidrains repo.
Differently from the script above, I used the transforms from the official repo. Because our training images were already cropped and aligned. official paper implementation repo
https://huggingface.co/ceyda/butterfly_cropped_uniq1K_512
Model description
Based on paper: Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
which states: "Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU, and has a consistent performance, even with less than 100 training samples"
also dubbed the Light-GAN model. This model was trained using the script here which is adapted from the lucidrains repo.
Differently from the script above, I used the transforms from the official repo. Because our training images were already cropped and aligned. official paper implementation repo
https://huggingface.co/ceyda/butterfly_cropped_uniq1K_512
huggingface.co
ceyda/butterfly_cropped_uniq1K_512 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Generate fauvism still life image using FastGAN
Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model was able to converge after some hours of training for either 100 high-quality images or 1000 images datasets.
This model was trained on a dataset of 124 high-quality Fauvism painting images.
How to use：https://huggingface.co/huggan/fastgan-few-shot-fauvism-still-life
Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model was able to converge after some hours of training for either 100 high-quality images or 1000 images datasets.
This model was trained on a dataset of 124 high-quality Fauvism painting images.
How to use：https://huggingface.co/huggan/fastgan-few-shot-fauvism-still-life
huggingface.co
huggan/fastgan-few-shot-fauvism-still-life · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
gaIA: Italian Landscape GAN Model
gaIA is the first Italian GAN model trained on satellite images of a selection of Italy's main glaciers, forests, lakes, rivers, and coasts that are most affected by climate change. It is usable for scientific and artistic purposes.
Dataset
Images: 12k
Image Format: 1024x1024
Source: Copernicus Sentinel 2A
Reference Years: 2017 – June 2024
gaIA is the first Italian GAN model trained on satellite images of a selection of Italy's main glaciers, forests, lakes, rivers, and coasts that are most affected by climate change. It is usable for scientific and artistic purposes.
Dataset
Images: 12k
Image Format: 1024x1024
Source: Copernicus Sentinel 2A
Reference Years: 2017 – June 2024
Riffusion: Optimized for Mobile Deployment
State-of-the-art generative AI model used to generate spectrogram images given any text input. These spectrograms can be converted into audio clips
Generates high resolution spectrograms images from text prompts using a latent diffusion model. This model uses CLIP ViT-L/14 as text encoder, U-Net based latent denoising, and VAE based decoder to generate the final image.
This model is an implementation of Riffusion found here. This repository provides scripts to run Riffusion on Qualcomm® devices. More details on model performance across various devices, can be found here.
State-of-the-art generative AI model used to generate spectrogram images given any text input. These spectrograms can be converted into audio clips
Generates high resolution spectrograms images from text prompts using a latent diffusion model. This model uses CLIP ViT-L/14 as text encoder, U-Net based latent denoising, and VAE based decoder to generate the final image.
This model is an implementation of Riffusion found here. This repository provides scripts to run Riffusion on Qualcomm® devices. More details on model performance across various devices, can be found here.
Stable-Diffusion: Optimized for Mobile Deployment
State-of-the-art generative AI model used to generate detailed images conditioned on text descriptions
Generates high resolution images from text prompts using a latent diffusion model. This model uses CLIP ViT-L/14 as text encoder, U-Net based latent denoising, and VAE based decoder to generate the final image.
This model is an implementation of Stable-Diffusion found here. This repository provides scripts to run Stable-Diffusion on Qualcomm® devices. More details on model performance across various devices, can be found here.
—Model Details—
-Model Type: Image generation
-Model Stats:
**Input: Text prompt to generate image
**QNN-SDK: 2.19
**Text Encoder Number of parameters: 340M
**UNet Number of parameters: 865M
**VAE Decoder Number of parameters: 83M
**Model size: 1GB
State-of-the-art generative AI model used to generate detailed images conditioned on text descriptions
Generates high resolution images from text prompts using a latent diffusion model. This model uses CLIP ViT-L/14 as text encoder, U-Net based latent denoising, and VAE based decoder to generate the final image.
This model is an implementation of Stable-Diffusion found here. This repository provides scripts to run Stable-Diffusion on Qualcomm® devices. More details on model performance across various devices, can be found here.
—Model Details—
-Model Type: Image generation
-Model Stats:
**Input: Text prompt to generate image
**QNN-SDK: 2.19
**Text Encoder Number of parameters: 340M
**UNet Number of parameters: 865M
**VAE Decoder Number of parameters: 83M
**Model size: 1GB
Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution.
Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reverse-time SDE depends only on the time-dependent gradient field (\aka, score) of the perturbed data distribution.
The Keras code example on denoising diffusion implicit models (DDIM).
Model description
The model uses a U-Net with identical input and output dimensions. It progressively downsamples and upsamples its input image, adding skip connections between layers having the same resolution. The architecture is a simplified version of the architecture of DDPM. It consists of convolutional residual blocks and lacks attention layers. The network takes two inputs, the noisy images and the variances of their noise components, which it encodes using sinusoidal embeddings.
https://huggingface.co/keras-io/denoising-diffusion-implicit-models
Model description
The model uses a U-Net with identical input and output dimensions. It progressively downsamples and upsamples its input image, adding skip connections between layers having the same resolution. The architecture is a simplified version of the architecture of DDPM. It consists of convolutional residual blocks and lacks attention layers. The network takes two inputs, the noisy images and the variances of their noise components, which it encodes using sinusoidal embeddings.
https://huggingface.co/keras-io/denoising-diffusion-implicit-models
Act as a Debater #chatgpt-prompts
Contributed by: @devisasari
I want you to act as a debater. I will provide you with some topics related to current events and your task is to research both sides of the debates, present valid arguments for each side, refute opposing points of view, and draw persuasive conclusions based on evidence. Your goal is to help people come away from the discussion with increased knowledge and insight into the topic at hand. My first request is "I want an opinion piece about Deno."
Contributed by: @devisasari
I want you to act as a debater. I will provide you with some topics related to current events and your task is to research both sides of the debates, present valid arguments for each side, refute opposing points of view, and draw persuasive conclusions based on evidence. Your goal is to help people come away from the discussion with increased knowledge and insight into the topic at hand. My first request is "I want an opinion piece about Deno."
Act as a Stand-up Comedian #chatgpt-prompts
Contributed by: @devisasari
I want you to act as a stand-up comedian. I will provide you with some topics related to current events and you will use your wit, creativity, and observational skills to create a routine based on those topics. You should also be sure to incorporate personal anecdotes or experiences into the routine in order to make it more relatable and engaging for the audience. My first request is "I want an humorous take on politics."
Contributed by: @devisasari
I want you to act as a stand-up comedian. I will provide you with some topics related to current events and you will use your wit, creativity, and observational skills to create a routine based on those topics. You should also be sure to incorporate personal anecdotes or experiences into the routine in order to make it more relatable and engaging for the audience. My first request is "I want an humorous take on politics."
Act as a Football Commentator #chatgpt-prompts
Contributed by: @devisasari
I want you to act as a football commentator. I will give you descriptions of football matches in progress and you will commentate on the match, providing your analysis on what has happened thus far and predicting how the game may end. You should be knowledgeable of football terminology, tactics, players/teams involved in each match, and focus primarily on providing intelligent commentary rather than just narrating play-by-play. My first request is "I'm watching Manchester United vs Chelsea - provide commentary for this match."
Contributed by: @devisasari
I want you to act as a football commentator. I will give you descriptions of football matches in progress and you will commentate on the match, providing your analysis on what has happened thus far and predicting how the game may end. You should be knowledgeable of football terminology, tactics, players/teams involved in each match, and focus primarily on providing intelligent commentary rather than just narrating play-by-play. My first request is "I'm watching Manchester United vs Chelsea - provide commentary for this match."
Act as a Storyteller #chatgpt-prompts
Contributed by: @devisasari
I want you to act as a storyteller. You will come up with entertaining stories that are engaging, imaginative and captivating for the audience. It can be fairy tales, educational stories or any other type of stories which has the potential to capture people's attention and imagination. Depending on the target audience, you may choose specific themes or topics for your storytelling session e.g., if it’s children then you can talk about animals; If it’s adults then history-based tales might engage them better etc. My first request is "I need an interesting story on perseverance."
Contributed by: @devisasari
I want you to act as a storyteller. You will come up with entertaining stories that are engaging, imaginative and captivating for the audience. It can be fairy tales, educational stories or any other type of stories which has the potential to capture people's attention and imagination. Depending on the target audience, you may choose specific themes or topics for your storytelling session e.g., if it’s children then you can talk about animals; If it’s adults then history-based tales might engage them better etc. My first request is "I need an interesting story on perseverance."
Act as an Advertiser #chatgpt-prompts
Contributed by: @devisasari
I want you to act as an advertiser. You will create a campaign to promote a product or service of your choice. You will choose a target audience, develop key messages and slogans, select the media channels for promotion, and decide on any additional activities needed to reach your goals. My first suggestion request is "I need help creating an advertising campaign for a new type of energy drink targeting young adults aged 18-30."
Contributed by: @devisasari
I want you to act as an advertiser. You will create a campaign to promote a product or service of your choice. You will choose a target audience, develop key messages and slogans, select the media channels for promotion, and decide on any additional activities needed to reach your goals. My first suggestion request is "I need help creating an advertising campaign for a new type of energy drink targeting young adults aged 18-30."