A curated collection of open-source Large Language Model (LLM) projects that are production-ready and can be used for solving real-world problems. This repository focuses on high-performance, scalable LLM solutions across various industries and applications.
- 🌟 Introduction
- 🎯 Purpose
- Awesome Lists
- Contributing
- License
With the rise of LLMs in various domains, there is a growing need for solutions that are ready for deployment in production environments. Awesome-LLM-Prod aims to provide a collection of open source, production-grade LLM repositories, tested and proven to scale, for real-world use cases. Whether you're deploying a large model for NLP tasks or integrating AI into a customer-facing product, this repository offers the tools and frameworks needed for real-world scenarios.
The purpose of this repository is to:
- Curate open-source LLM projects that are ready for production environments.
- Showcase real-world applications of LLMs across various industries.
- Provide solutions that focus on scalability, optimization, and deployment.
- Bridge the gap between research prototypes and production-grade projects.
- Production-ready LLM projects and implementations.
- Fine-tuning LLMs for specific tasks.
Project Name | Support | Tags | Description |
---|---|---|---|
Axolotl | Community | Training, Fine-Tuning | Tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures |
DeepSpeed | Microsoft | Training, Inference, Compression | An optimization library that makes distributed training and inference easy |
Hugging Face Transformers | Hugging Face | Training, Fine-Tuning, Inference, NLP | State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX |
LLaMA-Factory | Community | Training, Fine-Tuning | Unified Efficient Fine-Tuning of 100+ LLMs |
LitGPT | Lightning-AI | Training, Fine-Tuning, Deployment, Chatbots | 20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale |
Megatron-LM | NVIDIA | Training, Fine-Tuning | GPU optimized techniques for training transformer models at-scale |
ONNX Runtime | Microsoft | Inference, Training-Optimization | Cross-platform, high performance ML inferencing and training accelerator |
- Tools for inference, evaluating, testing, monitoring, and scaling LLMs.
- Deployment solutions for cloud and edge environments.
- Optimization techniques to reduce memory usage, latency, and costs.
Project Name | Support | Tags | Description |
---|---|---|---|
BentoML | BentoML | RAG, Model-Serving, API, Deployment | Framework for serving, managing, and deploying machine learning models |
LitServe | Lightning.AI | Inference, Model-Serving, Deployment | Lightning-fast serving engine for AI models |
MLflow | Databricks | Experiment Tracking, Model Registry, Deployment | An open source platform for the machine learning lifecycle |
OpenVINO | Intel | Inference, Optimization, Deployment | Toolkit for optimizing and deploying AI models across Intel hardware |
Ray | Anyscale | Distributed Computing, Scaling, Inference, Deployment | A unified framework for scaling AI and Python applications |
TensorRT-LLM | NVIDIA | Inference, Optimization | Optimize and deploy LLMs on NVIDIA GPUs |
Triton Inference Server | NVIDIA | Model-Serving, Inference, Deployment | Optimized and production-ready model inference server |
vllm | vllm-project | Inference, Deployment, Model-Serving | A high-throughput and memory-efficient inference and serving engine for LLMs |
Weights & Biases | Weights & Biases | Experiment Tracking, Visualization, Collaboration | MLOps platform for tracking experiments and managing machine learning projects |
- App Enablers
- Prompt optimizations
- Structured Output
- Projects applying LLMs to healthcare, finance, customer service, and other industries.
Project Name | Support | Tags | Description |
---|---|---|---|
AdalFlow | SylphAI-Inc | RAG, Agents, LLM Eval, Trainers, Optimizers | The library to build & auto-optimize any LLM task |
DSPy | StanfordNLP | RAG, Prompt-Optimization, Information-Extraction | Framework for programming—not prompting—foundation models |
Guidance | Microsoft | Templating, Generation-Control, Structured-Output | A guidance language for controlling LLMs |
Haystack | deepset-ai | RAG, Question-Answering, Information-Retrieval | End-to-end NLP framework for building applications powered by LLMs and Transformer models |
LangChain | langchain-ai | RAG, Structured-Output, Chatbots, Agents | LangChain is a framework for developing applications powered by LLMs |
LlamaIndex | Community | RAG, Data-Ingestion, Structured-Data | Data Framework for LLM applications to ingest, structure, and access private or domain-specific data |
mem0 | mem0ai | Memory-Layer | Enhances AI assistants and agents with an intelligent memory layer |
outlines | dottxt-ai | Structured-Output | Library for Structured Text Generation |
Semantic Kernel | Microsoft | AI-Orchestration, Plugins, Connectors, AI-services | Integrate cutting-edge LLM technology quickly and easily into your apps |
TTS | coqui-ai | Text-to-Speech | a deep learning toolkit for Text-to-Speech, battle-tested in research and production |
- Vector databases for efficient similarity search.
- Embedding tools for text-to-vector conversion.
- Indexing and retrieval solutions for large-scale datasets.
Project Name | Support | Tags | Description |
---|---|---|---|
Faiss | Facebook Research | Vector-Database, Similarity-Search | A library for efficient similarity search and clustering of dense vectors |
Milvus | Zilliz | Vector-Database | An open-source vector database built to power embedding similarity search |
Pinecone | Pinecone | Vector-Database | Managed vector database for machine learning applications |
Qdrant | Qdrant | Vector-Database, Rust | Vector similarity search engine and database |
sentence-transformers | UKPLab | Embeddings, Fine-Tuning, Multilingual | Provides an easy method to compute dense vector representations for sentences, paragraphs, and images |
Weaviate | SeMI Technologies | Vector-Database, GraphQL | Open source vector database that stores both objects and vectors |
- Tools for data generation, cleaning, preprocessing, and augmentation.
- Data versioning and lineage tracking solutions.
- High-quality datasets for training and fine-tuning LLMs in production environments.rew
Project Name | Support | Tags | Description |
---|---|---|---|
Argilla | Argilla-IO | Data-Generation, Data-Quality | collaboration tool for AI engineers and domain experts to build high-quality datasets |
DVC (Data Version Control) | Iterative | Data-Versioning, ML-Pipelines | Open-source version control system for machine learning projects |
Dolt | DoltHub | Data-Versioning, SQL-Database | Git for data: Version control system for structured data |
NeMo-Curator | NVIDIA | Data-Generation, Data-Processing, Scalability | Scalable data pre processing and curation toolkit for LLMs |
Pachyderm | Pachyderm | Data-Versioning, Data-Pipelines | Data-Centric Pipelines and Data Versioning |
Snorkel | Snorkel AI | Data -Labeling, Weak-Supervision | A system for programmatically building and managing training datasets |
** Note that some of the projects have overlapping categories, but have been classified based on intuitive understanding. If you think a different category better suits a project, please feel free to open a PR.
We welcome contributions from the community! If you know of any production-grade LLM project that fits our criteria, please feel free to open a pull request.
This repository is dedicated to the public domain under the Creative Commons CC0 1.0 Universal license. For more details, see the LICENSE
file or visit https://creativecommons.org/publicdomain/zero/1.0/.