meshidenn's Stars
openai/openai-cookbook
Examples and guides for using the OpenAI API
OpenInterpreter/open-interpreter
A natural language interface for computers
LAION-AI/Open-Assistant
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
rust-lang/book
The Rust Programming Language
stanfordnlp/dspy
DSPy: The framework for programming—not prompting—foundation models
VinciGit00/Scrapegraph-ai
Python scraper based on AI
AI4Finance-Foundation/FinGPT
FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
meta-llama/llama-recipes
Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization and Q&A. Supporting a number of candid inference solutions such as HF TGI, VLLM for local or cloud deployment. Demo apps to showcase Meta Llama3 for WhatsApp & Messenger.
vanna-ai/vanna
🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄.
tensorchord/Awesome-LLMOps
An awesome & curated list of best LLMOps tools for developers
microsoft/table-transformer
Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images). This is also the official repository for the PubTables-1M dataset and GriTS evaluation metric.
ashvardanian/StringZilla
Up to 10x faster strings for C, C++, Python, Rust, and Swift, leveraging SWAR and SIMD on Arm Neon and x86 AVX2 & AVX-512-capable chips to accelerate search, sort, edit distances, alignment scores, etc 🦖
apple/axlearn
An Extensible Deep Learning Library
gkamradt/LLMTest_NeedleInAHaystack
Doing simple retrieval from LLM models at various context lengths to measure accuracy
google-research/tapas
End-to-end neural table-text understanding models.
lmmlzn/Awesome-LLMs-Datasets
Summarize existing representative LLMs text datasets.
yuhattor/copilot-patterns
This document is a compilation of best practices for AI-Native development, curated by our community. Discover useful tips and tricks for leveraging tools to improve your AI development process.
o19s/hello-ltr
Set of Jupyter notebooks demonstrating Learning to Rank integrated with Solr and Elasticsearch
hiroshi-matsuda-rit/NLP2024-tutorial-3
NLP2024 チュートリアル3 作って学ぶ日本語大規模言語モデル - 環境構築手順とソースコード / NLP2024 Tutorial 3: Practicing how to build a Japanese large-scale language model - Environment construction and experimental source codes
EleutherAI/dps
Data processing system for polyglot
AUGMXNT/shisa
ryogrid/dbms-jisaku
contents of https://ryogrid.github.io/dbms-jisaku/
ubie-oss/esqa
Testing tool to verify the search qualities of the Elasticsearch indices
llm-jp/llm-jp-tokenizer
laboroai/Laboro-ParaCorpus
Scripts for creating a Japanese-English parallel corpus and training NMT models
sacabench/sacabench
Implementation of the sacabench framework
hitoshizuku7/awesome-Ja-self-instruct
cynthia/sse
Simple tool to allow an annotator to look at a source sentence and pick the most similar sentence out of a set of sentences.
Katsumata420/eda_nlp
Data augmentation for NLP, presented at EMNLP 2019
Katsumata420/sumeval
Well tested & Multi-language evaluation framework for text summarization.