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Harpreet Sahota PRO
harpreetsahota
AI & ML interests
Deep learning, laguage models, prompt engineering, agents, multi-agent systems
Recent Activity
updated
a dataset
about 5 hours ago
harpreetsahota/fisheye8k
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3 days ago
BGLab/BioTrove
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5 days ago
harpreetsahota/videos-to-test-trackers
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harpreetsahota's activity
reacted to
their
post with ๐ฅ๐
7 months ago
Post
2123
The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers.
I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.
The dataset consists of the following fields:
- An image of the first page of the paper
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Here's how I created the dataset ๐๐ผ
Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).
This dataset was built using the following steps:
- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract
Voxel51/CVPR_2024_Papers
I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.
The dataset consists of the following fields:
- An image of the first page of the paper
-
title: The title of the paper-
authors_list: The list of authors-
abstract: The abstract of the paper-
arxiv_link: Link to the paper on arXiv-
other_link: Link to the project page, if found-
category_name: The primary category this paper according to [arXiv taxonomy](https://arxiv.org/category_taxonomy)-
all_categories: All categories this paper falls into, according to arXiv taxonomy-
keywords: Extracted using GPT-4oHere's how I created the dataset ๐๐ผ
Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).
This dataset was built using the following steps:
- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract
Voxel51/CVPR_2024_Papers
posted
an
update
7 months ago
Post
2123
The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers.
I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.
The dataset consists of the following fields:
- An image of the first page of the paper
-
-
-
-
-
-
-
-
Here's how I created the dataset ๐๐ผ
Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).
This dataset was built using the following steps:
- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract
Voxel51/CVPR_2024_Papers
I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.
The dataset consists of the following fields:
- An image of the first page of the paper
-
title: The title of the paper-
authors_list: The list of authors-
abstract: The abstract of the paper-
arxiv_link: Link to the paper on arXiv-
other_link: Link to the project page, if found-
category_name: The primary category this paper according to [arXiv taxonomy](https://arxiv.org/category_taxonomy)-
all_categories: All categories this paper falls into, according to arXiv taxonomy-
keywords: Extracted using GPT-4oHere's how I created the dataset ๐๐ผ
Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).
This dataset was built using the following steps:
- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract
Voxel51/CVPR_2024_Papers
Dope!
reacted to
jamarks's
post with ๐คฏ๐ค๐ฅ๐
8 months ago
Post
2169
FiftyOne Datasets <> Hugging Face Hub Integration!
As of yesterday's release of FiftyOne
You can now load Parquet datasets from the hub and have them converted directly into FiftyOne datasets. To load MNIST, for example:
You can also load FiftyOne datasets directly from the hub. Here's how you load the first 1000 samples from the VisDrone dataset:
And tying it all together, you can push your FiftyOne datasets directly to the hub:
Major thanks to @tomaarsen @davanstrien @severo @osanseviero and @julien-c for helping to make this happen!!!
Full documentation and details here: https://docs.voxel51.com/integrations/huggingface.html#huggingface-hub
As of yesterday's release of FiftyOne
0.23.8, the FiftyOne open source library for dataset curation and visualization is now integrated with the Hugging Face Hub!You can now load Parquet datasets from the hub and have them converted directly into FiftyOne datasets. To load MNIST, for example:
pip install -U fiftyoneimport fiftyone as fo
import fiftyone.utils.huggingface as fouh
dataset = fouh.load_from_hub(
"mnist",
format="ParquetFilesDataset",
classification_fields="label",
)
session = fo.launch_app(dataset)You can also load FiftyOne datasets directly from the hub. Here's how you load the first 1000 samples from the VisDrone dataset:
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
dataset = fouh.load_from_hub("jamarks/VisDrone2019-DET", max_samples=1000)
# Launch the App
session = fo.launch_app(dataset)And tying it all together, you can push your FiftyOne datasets directly to the hub:
import fiftyone.zoo as foz
import fiftyone.utils.huggingface as fouh
dataset = foz.load_zoo_dataset("quickstart")
fouh.push_to_hub(dataset, "my-dataset")Major thanks to @tomaarsen @davanstrien @severo @osanseviero and @julien-c for helping to make this happen!!!
Full documentation and details here: https://docs.voxel51.com/integrations/huggingface.html#huggingface-hub
-
- 3 replies
reacted to
danielhanchen's
post with โค๏ธ
10 months ago
Post
Gemma QLoRA finetuning is now 2.4x faster and uses 58% less VRAM than FA2 through ๐ฆฅUnsloth! Had to rewrite our Cross Entropy Loss kernels to work on all vocab sizes, re-design our manual autograd engine to accept all activation functions, and more! I wrote all about our learnings in our blog post: https://unsloth.ai/blog/gemma.
Also have a Colab notebook with no OOMs, and has 2x faster inference for Gemma & how to merge and save to llama.cpp GGUF & vLLM: https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing
And uploaded 4bit pre-quantized versions for Gemma 2b and 7b: unsloth/gemma-7b-bnb-4bit unsloth/gemma-2b-bnb-4bit
Also have a Colab notebook with no OOMs, and has 2x faster inference for Gemma & how to merge and save to llama.cpp GGUF & vLLM: https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing
And uploaded 4bit pre-quantized versions for Gemma 2b and 7b: unsloth/gemma-7b-bnb-4bit unsloth/gemma-2b-bnb-4bit
from unsloth import FastLanguageModel
model, tokenzer = FastLanguageModel.from_pretrained("unsloth/gemma-7b")
model = FastLanguageModel.get_peft_model(model)
- 4 replies
reacted to
their
post with โค๏ธ
10 months ago
Post
google/gemma-7b-it is super good!
I wasn't convinced at first, but after vibe-checking it...I'm quite impressed.
I've got a notebook here, which is kind of a framework for vibe-checking LLMs.
In this notebook, I take Gemma for a spin on a variety of prompts:
โข [nonsensical tokens]( harpreetsahota/diverse-token-sampler
โข [conversation where I try to get some PII)( harpreetsahota/red-team-prompts-questions)
โข [summarization ability]( lighteval/summarization)
โข [instruction following]( harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models
โข [chain of thought reasoning]( ssbuild/alaca_chain-of-thought)
I then used LangChain evaluators (GPT-4 as judge), and track everything in LangSmith. I made public links to the traces where you can inspect the runs.
I hope you find this helpful, and I am certainly open to feedback, criticisms, or ways to improve.
Cheers:
You can find the notebook here: https://colab.research.google.com/drive/1RHzg0FD46kKbiGfTdZw9Fo-DqWzajuoi?usp=sharing
I wasn't convinced at first, but after vibe-checking it...I'm quite impressed.
I've got a notebook here, which is kind of a framework for vibe-checking LLMs.
In this notebook, I take Gemma for a spin on a variety of prompts:
โข [nonsensical tokens]( harpreetsahota/diverse-token-sampler
โข [conversation where I try to get some PII)( harpreetsahota/red-team-prompts-questions)
โข [summarization ability]( lighteval/summarization)
โข [instruction following]( harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models
โข [chain of thought reasoning]( ssbuild/alaca_chain-of-thought)
I then used LangChain evaluators (GPT-4 as judge), and track everything in LangSmith. I made public links to the traces where you can inspect the runs.
I hope you find this helpful, and I am certainly open to feedback, criticisms, or ways to improve.
Cheers:
You can find the notebook here: https://colab.research.google.com/drive/1RHzg0FD46kKbiGfTdZw9Fo-DqWzajuoi?usp=sharing
reacted to
merve's
post with ๐
10 months ago
Post
I've tried DoRA (https://arxiv.org/abs/2402.09353) with SDXL using PEFT, outputs are quite detailed ๐คฉ๐
as usual trained on lego dataset I compiled, I compared them with previously trained pivotal tuned model and the normal DreamBooth model before that ๐
Notebook by @linoyts https://colab.research.google.com/drive/134mt7bCMKtCYyYzETfEGKXT1J6J50ydT?usp=sharing
Integration to PEFT by @BenjaminB https://github.com/huggingface/peft/pull/1474 (more info in the PR)
as usual trained on lego dataset I compiled, I compared them with previously trained pivotal tuned model and the normal DreamBooth model before that ๐
Notebook by @linoyts https://colab.research.google.com/drive/134mt7bCMKtCYyYzETfEGKXT1J6J50ydT?usp=sharing
Integration to PEFT by @BenjaminB https://github.com/huggingface/peft/pull/1474 (more info in the PR)
reacted to
Wauplin's
post with ๐๐คโค๏ธ
10 months ago
Post
๐ Just released version 0.21.0 of the
Exciting updates include:
๐๏ธ Dataclasses everywhere for improved developer experience!
๐พ HfFileSystem optimizations!
๐งฉ
โจ
๐ Translated docs in Simplified Chinese and French!
๐ Breaking changes: simplified API for listing models and datasets!
Check out the full release notes for more details: Wauplin/huggingface_hub#4 ๐ค๐ป
huggingface_hub Python library!Exciting updates include:
๐๏ธ Dataclasses everywhere for improved developer experience!
๐พ HfFileSystem optimizations!
๐งฉ
PyTorchHubMixin now supports configs and safetensors!โจ
audio-to-audio supported in the InferenceClient!๐ Translated docs in Simplified Chinese and French!
๐ Breaking changes: simplified API for listing models and datasets!
Check out the full release notes for more details: Wauplin/huggingface_hub#4 ๐ค๐ป
- 4 replies
reacted to
clem's
post with โค๏ธ
10 months ago
Post
Terribly excited about open-source + on-device AI these days! Great to see
@qualcomm
release 80+ models optimized and curated for their devices and chips on HF: https://huggingface.co/qualcomm
-
- 1 reply
posted
an
update
10 months ago
Post
google/gemma-7b-it is super good!
I wasn't convinced at first, but after vibe-checking it...I'm quite impressed.
I've got a notebook here, which is kind of a framework for vibe-checking LLMs.
In this notebook, I take Gemma for a spin on a variety of prompts:
โข [nonsensical tokens]( harpreetsahota/diverse-token-sampler
โข [conversation where I try to get some PII)( harpreetsahota/red-team-prompts-questions)
โข [summarization ability]( lighteval/summarization)
โข [instruction following]( harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models
โข [chain of thought reasoning]( ssbuild/alaca_chain-of-thought)
I then used LangChain evaluators (GPT-4 as judge), and track everything in LangSmith. I made public links to the traces where you can inspect the runs.
I hope you find this helpful, and I am certainly open to feedback, criticisms, or ways to improve.
Cheers:
You can find the notebook here: https://colab.research.google.com/drive/1RHzg0FD46kKbiGfTdZw9Fo-DqWzajuoi?usp=sharing
I wasn't convinced at first, but after vibe-checking it...I'm quite impressed.
I've got a notebook here, which is kind of a framework for vibe-checking LLMs.
In this notebook, I take Gemma for a spin on a variety of prompts:
โข [nonsensical tokens]( harpreetsahota/diverse-token-sampler
โข [conversation where I try to get some PII)( harpreetsahota/red-team-prompts-questions)
โข [summarization ability]( lighteval/summarization)
โข [instruction following]( harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models
โข [chain of thought reasoning]( ssbuild/alaca_chain-of-thought)
I then used LangChain evaluators (GPT-4 as judge), and track everything in LangSmith. I made public links to the traces where you can inspect the runs.
I hope you find this helpful, and I am certainly open to feedback, criticisms, or ways to improve.
Cheers:
You can find the notebook here: https://colab.research.google.com/drive/1RHzg0FD46kKbiGfTdZw9Fo-DqWzajuoi?usp=sharing
reacted to
philschmid's
post with โค๏ธ
11 months ago
Post
What's the best way to fine-tune open LLMs in 2024? Look no further! ๐ย I am excited to share โHow to Fine-Tune LLMs in 2024 with Hugging Faceโ using the latest research techniques, including Flash Attention, Q-LoRA, OpenAI dataset formats (messages), ChatML, Packing, all built with Hugging Face TRL. ๐
It is created for consumer-size GPUs (24GB) covering the full end-to-end lifecycle with:
๐กDefine and understand use cases for fine-tuning
๐ง๐ปโ๐ปย Setup of the development environment
๐งฎย Create and prepare dataset (OpenAI format)
๐๏ธโโ๏ธย Fine-tune LLM using TRL and the SFTTrainer
๐ฅย Test and evaluate the LLM
๐ย Deploy for production with TGI
๐ย https://www.philschmid.de/fine-tune-llms-in-2024-with-trl
Coming soon: Advanced Guides for multi-GPU/multi-Node full fine-tuning and alignment using DPO & KTO. ๐
It is created for consumer-size GPUs (24GB) covering the full end-to-end lifecycle with:
๐กDefine and understand use cases for fine-tuning
๐ง๐ปโ๐ปย Setup of the development environment
๐งฎย Create and prepare dataset (OpenAI format)
๐๏ธโโ๏ธย Fine-tune LLM using TRL and the SFTTrainer
๐ฅย Test and evaluate the LLM
๐ย Deploy for production with TGI
๐ย https://www.philschmid.de/fine-tune-llms-in-2024-with-trl
Coming soon: Advanced Guides for multi-GPU/multi-Node full fine-tuning and alignment using DPO & KTO. ๐
- 4 replies
reacted to
abidlabs's
post with ๐คโค๏ธ
11 months ago
Post
๐๐ฆ๐๐ซ๐๐๐๐ ๐๐ฒ ๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐
๐๐๐: ๐ญ๐ก๐ ๐๐ง๐ฌ๐ข๐๐ ๐๐ญ๐จ๐ซ๐ฒ ๐จ๐ ๐๐ฎ๐ซ ๐๐ญ๐๐ซ๐ญ๐ฎ๐ฉโ๐ฌ ๐๐๐ช๐ฎ๐ข๐ฌ๐ข๐ญ๐ข๐จ๐ง
In late 2021, our team of five engineers, scattered around the globe, signed the papers to shut down our startup, Gradio. For many founders, this would have been a moment of sadness or even bitter reflection.
But we were celebrating. We were getting acquired by Hugging Face!
We had been working very hard towards this acquisition, but for weeks, the acquisition had been blocked by a single investor. The more we pressed him, the more he buckled down, refusing to sign off on the acquisition. Until, unexpectedly, the investor conceded, allowing us to join Hugging Face.
For the first time since our acquisition, Iโm writing down the story in detail, hoping that it may shed some light into the obscure world of startup acquisitions and what decisions founders can make to improve their odds for a successful acquisition.
To understand how we got acquired by Hugging Face, you need to know why we started Gradio.
๐๐ง ๐๐๐๐ ๐๐ซ๐จ๐ฆ ๐ญ๐ก๐ ๐๐๐๐ซ๐ญ
Two years before the acquisition, in early 2019, I was working on a research project at Stanford. It was the third year of my PhD, and my labmates and I had trained a machine learning model that could predict patient biomarkers (such as whether patients had certain diseases or an implanted pacemaker) from an ultrasound image of their heart โ as well as a cardiologist.
Naturally, cardiologists were skeptical... read the rest of the story here: https://twitter.com/abidlabs/status/1745533306492588303
In late 2021, our team of five engineers, scattered around the globe, signed the papers to shut down our startup, Gradio. For many founders, this would have been a moment of sadness or even bitter reflection.
But we were celebrating. We were getting acquired by Hugging Face!
We had been working very hard towards this acquisition, but for weeks, the acquisition had been blocked by a single investor. The more we pressed him, the more he buckled down, refusing to sign off on the acquisition. Until, unexpectedly, the investor conceded, allowing us to join Hugging Face.
For the first time since our acquisition, Iโm writing down the story in detail, hoping that it may shed some light into the obscure world of startup acquisitions and what decisions founders can make to improve their odds for a successful acquisition.
To understand how we got acquired by Hugging Face, you need to know why we started Gradio.
๐๐ง ๐๐๐๐ ๐๐ซ๐จ๐ฆ ๐ญ๐ก๐ ๐๐๐๐ซ๐ญ
Two years before the acquisition, in early 2019, I was working on a research project at Stanford. It was the third year of my PhD, and my labmates and I had trained a machine learning model that could predict patient biomarkers (such as whether patients had certain diseases or an implanted pacemaker) from an ultrasound image of their heart โ as well as a cardiologist.
Naturally, cardiologists were skeptical... read the rest of the story here: https://twitter.com/abidlabs/status/1745533306492588303
- 1 reply
posted
an
update
11 months ago
Post
โ๐ผTwo new models dropped today ๐๐ฝ
1) ๐ฉ๐พโ๐ป ๐๐๐๐ข๐๐จ๐๐๐ซ-๐๐
๐๐ฝ Supports ๐ ๐ฅ๐๐ง๐ ๐ฎ๐๐ ๐๐ฌ: C, C# C++, GO, Rust, Python, Java, and Javascript.
๐๐ฝ Released under the ๐๐ฉ๐๐๐ก๐ ๐.๐ ๐ฅ๐ข๐๐๐ง๐ฌ๐
๐ฅ ๐๐ฎ๐ง๐๐ก๐๐ฌ ๐๐๐จ๐ฏ๐ ๐ข๐ญ๐ฌ ๐ฐ๐๐ข๐ ๐ก๐ญ ๐๐ฅ๐๐ฌ๐ฌ ๐จ๐ง ๐๐ฎ๐ฆ๐๐ง๐๐ฏ๐๐ฅ: Beats out CodeGen 2.5 7B and StarCoder 7B on most supported languages. Has a 3-point lead over StarCoderBase 15.5B for Python
๐ป ๐ป๐๐ ๐๐ ๐๐๐:
๐ ๐๐จ๐๐๐ฅ ๐๐๐ซ๐: Deci/DeciCoder-6B
๐ ๐๐จ๐ญ๐๐๐จ๐จ๐ค: https://colab.research.google.com/drive/1QRbuser0rfUiFmQbesQJLXVtBYZOlKpB
๐ชง ๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐ ๐๐๐ ๐๐ฉ๐๐๐: Deci/DeciCoder-6B-Demo
2) ๐จ ๐๐๐๐ข๐๐ข๐๐๐ฎ๐ฌ๐ข๐จ๐ง ๐ฏ๐.๐
๐๐ฝ Produces quality images on par with Stable Diffusion v1.5, but ๐.๐ ๐ญ๐ข๐ฆ๐๐ฌ ๐๐๐ฌ๐ญ๐๐ซ ๐ข๐ง ๐๐% ๐๐๐ฐ๐๐ซ ๐ข๐ญ๐๐ซ๐๐ญ๐ข๐จ๐ง๐ฌ
๐๐ฝ Employs a ๐ฌ๐ฆ๐๐ฅ๐ฅ๐๐ซ ๐๐ง๐ ๐๐๐ฌ๐ญ๐๐ซ ๐-๐๐๐ญ ๐๐จ๐ฆ๐ฉ๐จ๐ง๐๐ง๐ญ ๐ฐ๐ก๐ข๐๐ก ๐ก๐๐ฌ ๐๐๐ ๐ฆ๐ข๐ฅ๐ฅ๐ข๐จ๐ง ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ๐ฌ.
๐๐ฝ Uses an optimized scheduler, ๐๐ช๐ฎ๐๐๐ณ๐๐๐๐๐++, which ๐๐ฎ๐ญ๐ฌ ๐๐จ๐ฐ๐ง ๐ญ๐ก๐ ๐ง๐ฎ๐ฆ๐๐๐ซ ๐จ๐ ๐ฌ๐ญ๐๐ฉ๐ฌ ๐ง๐๐๐๐๐ ๐ญ๐จ ๐ ๐๐ง๐๐ซ๐๐ญ๐ ๐ ๐ช๐ฎ๐๐ฅ๐ข๐ญ๐ฒ ๐ข๐ฆ๐๐ ๐ ๐๐ซ๐จ๐ฆ ๐๐ ๐ญ๐จ ๐๐.
๐๐ฝ Released under the ๐๐ซ๐๐๐ญ๐ข๐ฏ๐๐๐ ๐๐ฉ๐๐ง ๐๐๐๐++-๐ ๐๐ข๐๐๐ง๐ฌ๐.
๐ป ๐ป๐๐ ๐๐ ๐๐๐:
๐ ๐๐จ๐๐๐ฅ ๐๐๐ซ๐: Deci/DeciDiffusion-v2-0
๐ ๐๐จ๐ญ๐๐๐จ๐จ๐ค: https://colab.research.google.com/drive/11Ui_KRtK2DkLHLrW0aa11MiDciW4dTuB
๐ชง ๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐ ๐๐๐ ๐๐ฉ๐๐๐: Deci/DeciDiffusion-v2-0
Help support the projects by liking the model cards and the spaces!
Cheers and happy hacking!
1) ๐ฉ๐พโ๐ป ๐๐๐๐ข๐๐จ๐๐๐ซ-๐๐
๐๐ฝ Supports ๐ ๐ฅ๐๐ง๐ ๐ฎ๐๐ ๐๐ฌ: C, C# C++, GO, Rust, Python, Java, and Javascript.
๐๐ฝ Released under the ๐๐ฉ๐๐๐ก๐ ๐.๐ ๐ฅ๐ข๐๐๐ง๐ฌ๐
๐ฅ ๐๐ฎ๐ง๐๐ก๐๐ฌ ๐๐๐จ๐ฏ๐ ๐ข๐ญ๐ฌ ๐ฐ๐๐ข๐ ๐ก๐ญ ๐๐ฅ๐๐ฌ๐ฌ ๐จ๐ง ๐๐ฎ๐ฆ๐๐ง๐๐ฏ๐๐ฅ: Beats out CodeGen 2.5 7B and StarCoder 7B on most supported languages. Has a 3-point lead over StarCoderBase 15.5B for Python
๐ป ๐ป๐๐ ๐๐ ๐๐๐:
๐ ๐๐จ๐๐๐ฅ ๐๐๐ซ๐: Deci/DeciCoder-6B
๐ ๐๐จ๐ญ๐๐๐จ๐จ๐ค: https://colab.research.google.com/drive/1QRbuser0rfUiFmQbesQJLXVtBYZOlKpB
๐ชง ๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐ ๐๐๐ ๐๐ฉ๐๐๐: Deci/DeciCoder-6B-Demo
2) ๐จ ๐๐๐๐ข๐๐ข๐๐๐ฎ๐ฌ๐ข๐จ๐ง ๐ฏ๐.๐
๐๐ฝ Produces quality images on par with Stable Diffusion v1.5, but ๐.๐ ๐ญ๐ข๐ฆ๐๐ฌ ๐๐๐ฌ๐ญ๐๐ซ ๐ข๐ง ๐๐% ๐๐๐ฐ๐๐ซ ๐ข๐ญ๐๐ซ๐๐ญ๐ข๐จ๐ง๐ฌ
๐๐ฝ Employs a ๐ฌ๐ฆ๐๐ฅ๐ฅ๐๐ซ ๐๐ง๐ ๐๐๐ฌ๐ญ๐๐ซ ๐-๐๐๐ญ ๐๐จ๐ฆ๐ฉ๐จ๐ง๐๐ง๐ญ ๐ฐ๐ก๐ข๐๐ก ๐ก๐๐ฌ ๐๐๐ ๐ฆ๐ข๐ฅ๐ฅ๐ข๐จ๐ง ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ๐ฌ.
๐๐ฝ Uses an optimized scheduler, ๐๐ช๐ฎ๐๐๐ณ๐๐๐๐๐++, which ๐๐ฎ๐ญ๐ฌ ๐๐จ๐ฐ๐ง ๐ญ๐ก๐ ๐ง๐ฎ๐ฆ๐๐๐ซ ๐จ๐ ๐ฌ๐ญ๐๐ฉ๐ฌ ๐ง๐๐๐๐๐ ๐ญ๐จ ๐ ๐๐ง๐๐ซ๐๐ญ๐ ๐ ๐ช๐ฎ๐๐ฅ๐ข๐ญ๐ฒ ๐ข๐ฆ๐๐ ๐ ๐๐ซ๐จ๐ฆ ๐๐ ๐ญ๐จ ๐๐.
๐๐ฝ Released under the ๐๐ซ๐๐๐ญ๐ข๐ฏ๐๐๐ ๐๐ฉ๐๐ง ๐๐๐๐++-๐ ๐๐ข๐๐๐ง๐ฌ๐.
๐ป ๐ป๐๐ ๐๐ ๐๐๐:
๐ ๐๐จ๐๐๐ฅ ๐๐๐ซ๐: Deci/DeciDiffusion-v2-0
๐ ๐๐จ๐ญ๐๐๐จ๐จ๐ค: https://colab.research.google.com/drive/11Ui_KRtK2DkLHLrW0aa11MiDciW4dTuB
๐ชง ๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐ ๐๐๐ ๐๐ฉ๐๐๐: Deci/DeciDiffusion-v2-0
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