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llm-wizard
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llm-wizard
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llm-wizard
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llm-wizard
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5 months ago
harpreetsahota
posted
an
update
8 months ago
Post
2209
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 👇🏼
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 👇🏼
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
llm-wizard
updated
4
models
9 months ago
llm-wizard
updated
2
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10 months ago
llm-wizard
updated
a
dataset
10 months ago
llm-wizard
updated
3
models
10 months ago
harpreetsahota
posted
an
update
12 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:
• [nonsensical tokens]( harpreetsahota/diverse-token-sampler
• [conversation where I try to get some PII)( harpreetsahota/red-team-prompts-questions)
• [summarization ability]( lighteval/summarization)
• [instruction following]( harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models
• [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:
• [nonsensical tokens]( harpreetsahota/diverse-token-sampler
• [conversation where I try to get some PII)( harpreetsahota/red-team-prompts-questions)
• [summarization ability]( lighteval/summarization)
• [instruction following]( harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models
• [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
harpreetsahota
posted
an
update
about 1 year ago
Post
✌🏼Two new models dropped today 👇🏽
1) 👩🏾💻 𝐃𝐞𝐜𝐢𝐂𝐨𝐝𝐞𝐫-𝟔𝐁
👉🏽 Supports 𝟖 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬: C, C# C++, GO, Rust, Python, Java, and Javascript.
👉🏽 Released under the 𝐀𝐩𝐚𝐜𝐡𝐞 𝟐.𝟎 𝐥𝐢𝐜𝐞𝐧𝐬𝐞
🥊 𝐏𝐮𝐧𝐜𝐡𝐞𝐬 𝐚𝐛𝐨𝐯𝐞 𝐢𝐭𝐬 𝐰𝐞𝐢𝐠𝐡𝐭 𝐜𝐥𝐚𝐬𝐬 𝐨𝐧 𝐇𝐮𝐦𝐚𝐧𝐄𝐯𝐚𝐥: Beats out CodeGen 2.5 7B and StarCoder 7B on most supported languages. Has a 3-point lead over StarCoderBase 15.5B for Python
💻 𝑻𝒓𝒚 𝒊𝒕 𝒐𝒖𝒕:
🃏 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝: Deci/DeciCoder-6B
📓 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤: https://colab.research.google.com/drive/1QRbuser0rfUiFmQbesQJLXVtBYZOlKpB
🪧 𝐇𝐮𝐠𝐠𝐢𝐧𝐠𝐅𝐚𝐜𝐞 𝐒𝐩𝐚𝐜𝐞: Deci/DeciCoder-6B-Demo
2) 🎨 𝐃𝐞𝐜𝐢𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧 𝐯𝟐.𝟎
👉🏽 Produces quality images on par with Stable Diffusion v1.5, but 𝟐.𝟔 𝐭𝐢𝐦𝐞𝐬 𝐟𝐚𝐬𝐭𝐞𝐫 𝐢𝐧 𝟒𝟎% 𝐟𝐞𝐰𝐞𝐫 𝐢𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬
👉🏽 Employs a 𝐬𝐦𝐚𝐥𝐥𝐞𝐫 𝐚𝐧𝐝 𝐟𝐚𝐬𝐭𝐞𝐫 𝐔-𝐍𝐞𝐭 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭 𝐰𝐡𝐢𝐜𝐡 𝐡𝐚𝐬 𝟖𝟔𝟎 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬.
👉🏽 Uses an optimized scheduler, 𝐒𝐪𝐮𝐞𝐞𝐳𝐞𝐝𝐃𝐏𝐌++, which 𝐜𝐮𝐭𝐬 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐬𝐭𝐞𝐩𝐬 𝐧𝐞𝐞𝐝𝐞𝐝 𝐭𝐨 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐦𝐚𝐠𝐞 𝐟𝐫𝐨𝐦 𝟏𝟔 𝐭𝐨 𝟏𝟎.
👉🏽 Released under the 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐞𝐌𝐋 𝐎𝐩𝐞𝐧 𝐑𝐀𝐈𝐋++-𝐌 𝐋𝐢𝐜𝐞𝐧𝐬𝐞.
💻 𝑻𝒓𝒚 𝒊𝒕 𝒐𝒖𝒕:
🃏 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝: Deci/DeciDiffusion-v2-0
📓 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤: https://colab.research.google.com/drive/11Ui_KRtK2DkLHLrW0aa11MiDciW4dTuB
🪧 𝐇𝐮𝐠𝐠𝐢𝐧𝐠𝐅𝐚𝐜𝐞 𝐒𝐩𝐚𝐜𝐞: Deci/DeciDiffusion-v2-0
Help support the projects by liking the model cards and the spaces!
Cheers and happy hacking!
1) 👩🏾💻 𝐃𝐞𝐜𝐢𝐂𝐨𝐝𝐞𝐫-𝟔𝐁
👉🏽 Supports 𝟖 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬: C, C# C++, GO, Rust, Python, Java, and Javascript.
👉🏽 Released under the 𝐀𝐩𝐚𝐜𝐡𝐞 𝟐.𝟎 𝐥𝐢𝐜𝐞𝐧𝐬𝐞
🥊 𝐏𝐮𝐧𝐜𝐡𝐞𝐬 𝐚𝐛𝐨𝐯𝐞 𝐢𝐭𝐬 𝐰𝐞𝐢𝐠𝐡𝐭 𝐜𝐥𝐚𝐬𝐬 𝐨𝐧 𝐇𝐮𝐦𝐚𝐧𝐄𝐯𝐚𝐥: Beats out CodeGen 2.5 7B and StarCoder 7B on most supported languages. Has a 3-point lead over StarCoderBase 15.5B for Python
💻 𝑻𝒓𝒚 𝒊𝒕 𝒐𝒖𝒕:
🃏 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝: Deci/DeciCoder-6B
📓 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤: https://colab.research.google.com/drive/1QRbuser0rfUiFmQbesQJLXVtBYZOlKpB
🪧 𝐇𝐮𝐠𝐠𝐢𝐧𝐠𝐅𝐚𝐜𝐞 𝐒𝐩𝐚𝐜𝐞: Deci/DeciCoder-6B-Demo
2) 🎨 𝐃𝐞𝐜𝐢𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧 𝐯𝟐.𝟎
👉🏽 Produces quality images on par with Stable Diffusion v1.5, but 𝟐.𝟔 𝐭𝐢𝐦𝐞𝐬 𝐟𝐚𝐬𝐭𝐞𝐫 𝐢𝐧 𝟒𝟎% 𝐟𝐞𝐰𝐞𝐫 𝐢𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬
👉🏽 Employs a 𝐬𝐦𝐚𝐥𝐥𝐞𝐫 𝐚𝐧𝐝 𝐟𝐚𝐬𝐭𝐞𝐫 𝐔-𝐍𝐞𝐭 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭 𝐰𝐡𝐢𝐜𝐡 𝐡𝐚𝐬 𝟖𝟔𝟎 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬.
👉🏽 Uses an optimized scheduler, 𝐒𝐪𝐮𝐞𝐞𝐳𝐞𝐝𝐃𝐏𝐌++, which 𝐜𝐮𝐭𝐬 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐬𝐭𝐞𝐩𝐬 𝐧𝐞𝐞𝐝𝐞𝐝 𝐭𝐨 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐦𝐚𝐠𝐞 𝐟𝐫𝐨𝐦 𝟏𝟔 𝐭𝐨 𝟏𝟎.
👉🏽 Released under the 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐞𝐌𝐋 𝐎𝐩𝐞𝐧 𝐑𝐀𝐈𝐋++-𝐌 𝐋𝐢𝐜𝐞𝐧𝐬𝐞.
💻 𝑻𝒓𝒚 𝒊𝒕 𝒐𝒖𝒕:
🃏 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝: Deci/DeciDiffusion-v2-0
📓 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤: https://colab.research.google.com/drive/11Ui_KRtK2DkLHLrW0aa11MiDciW4dTuB
🪧 𝐇𝐮𝐠𝐠𝐢𝐧𝐠𝐅𝐚𝐜𝐞 𝐒𝐩𝐚𝐜𝐞: Deci/DeciDiffusion-v2-0
Help support the projects by liking the model cards and the spaces!
Cheers and happy hacking!
llm-wizard
updated
a
model
over 1 year ago