Large Language Models as Tool Makers

In this work, we take an initial step towards removing this dependency by proposing a closed-loop framework, referred to as LLMs A s Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving.

🖥 Github: https://github.com/ctlllll/llm-toolmaker

⏩ Paper: https://arxiv.org/pdf/2305.17126v1.pdf

📌 Dataset: https://paperswithcode.com/dataset/big-bench

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🦙 BigTrans 🚀

BigTrans which adapts LLaMA that covers only 20 languages and enhances it with multilingual translation capability on more than 100 languag

🖥 Github: https://github.com/ZNLP/BigTrans/tree/main

⏩ Paper: https://arxiv.org/abs/2305.18098v1

📌 Dataset: https://paperswithcode.com/dataset/flores-200

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🔥 GPT4Tools: Teaching LLM to Use Tools via Self-instruction

GPT4Tools is a centralized system that can control multiple visual foundation models. It is based on Vicuna (LLaMA), and 71K self-built instruction data.

🖥 Github: https://github.com/stevengrove/gpt4tools

⏩ Paper: https://arxiv.org/abs/2305.18752v1

📌 Project: https://gpt4tools.github.io/

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Introducing BERTopic Integration with the Hugging Face Hub

BERTopic provides a powerful tool for users to uncover significant topics within text collections, thereby gaining valuable insights.

pip install bertopic

🤗 Hugging face: https://huggingface.co/blog/bertopic

🖥 Github: https://github.com/MaartenGr/BERTopic

⏩ Colab: https://colab.research.google.com/#fileId=https://huggingface.co/spaces/davanstrien/blog_notebooks/blob/main/BERTopic_hub_starter.ipynb

📌 Docs: https://maartengr.github.io/BERTopic/getting_started/quickstart/quickstart.html

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Dynamic Sparse Training with Structured Sparsity

🖥 Github: https://github.com/calgaryml/condensed-sparsity

⏩ Paper: https://arxiv.org/pdf/2305.02299v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/cifar-10

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SSSegmenation

🖥 Github: https://github.com/segmentationblwx/sssegmentation

⏩ Paper: https://arxiv.org/pdf/2305.17091v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/cityscapes

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🔥 10 Free Machine Learning Courses from Top Universities

1. Introduction to Machine Learning - UC Berkeley

2. Introduction to Machine Learning - Carnegie Mellon University

3. Machine Learning - Stanford University

4. Machine Learning & Data Mining - Caltech

5. Learning from Data - Caltech

6. Machine Learning for Intelligent Systems - Cornell University

7. Large Scale Machine Learning - University of Toronto

8. Machine Learning with Large Datasets - Carnegie Mellon University

9. Foundations of Machine Learning and Statistical Inference - Caltech

10. Algorithmic Aspects of Machine Learning - MIT

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Daily books
https://news.1rj.ru/str/DataScience_Books
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Wuerstchen: Efficient Pretraining of Text-to-Image Models

Novel technique for text-to-image synthesis that unites competitive performance with unprecedented cost-effectiveness and ease of training on constrained hardwar

🖥 Github: https://github.com/dome272/wuerstchen

⏩ Paper: https://arxiv.org/abs/2306.00637v1

📌 Colab: https://colab.research.google.com/drive/1UTP9Xn2UIrVbAXyL-SKEvyLmgVWdw-Vy

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If you’re a developer wanting to use large language model tools, our new course is for you.

You’ll learn how to use different prompts at various stages in the system-building process, strategies for parsing long documents, and much more!

Join for free:
https://learn.deeplearning.ai/chatgpt-building-system

✅ More reaction = more posts

@CodeProgrammer ♥️

TabEAE

🖥 Github: https://github.com/stardust-hyx/tabeae

⏩ Paper: https://arxiv.org/pdf/2306.00502v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/wikievents

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🔭 GRES: Generalized Referring Expression Segmentation

New benchmark (GRES), which extends the classic RES to allow expressions to refer to an arbitrary number of target objects.

🖥 Github: https://github.com/henghuiding/ReLA

⏩ Paper: https://arxiv.org/abs/2306.00968

🔎 Project: https://henghuiding.github.io/GRES/

📌 New dataset: https://github.com/henghuiding/gRefCOCO

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🦍 Gorilla: Large Language Model Connected with Massive APIs

Gorilla a finetuned LLaMA-based model that surpasses the performance of GPT-4 on writing API calls.

🖥 Github: https://github.com/ShishirPatil/gorilla

📕 Paper: https://arxiv.org/abs/2305.15334

🔗 Demo: https://drive.google.com/file/d/1E0k5mG1mTiaz0kukyK1PdeohJipTFh6j/view?usp=share_link

👉 Project: https://shishirpatil.github.io/gorilla/

⭐️ Colab: https://colab.research.google.com/drive/1DEBPsccVLF_aUnmD0FwPeHFrtdC0QIUP?usp=sharing

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Segment Anything 3D

SAM-3D: A toolbox transfers 2D SAM segments into 3D scene-level point clouds.

🖥 Github: https://github.com/pointcept/segmentanything3d

⏩ Paper: https://arxiv.org/abs/2306.03908v1

📌 Dataset: https://paperswithcode.com/dataset/scannet

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🐼 PandaLM: ReProducible and Automated Language Model Assessment

Judge large language model, named PandaLM, which is trained to distinguish the superior model given several LLMs. PandaLM's focus extends beyond just the objective correctness of responses, which is the main focus of traditional evaluation datasets.

🖥 Github: https://github.com/weopenml/pandalm

📕 Paper: https://arxiv.org/abs/2306.05087v1

🔗 Dataset: https://github.com/tatsu-lab/stanford_alpaca#data-release

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