Dataset and Synthetic Data Tools
Build and clean training datasets, or generate synthetic ones with a model, using tools like Distilabel and Data-Juicer.
Open-source Python library for synthetic data generation and LLM training, with local models, API-based models, caching, and resumable workflows.
1.1KUpdated 2 years agoMIT
#Hugging Face integration#LoRA#Quantization
DataDreamer connects LLM prompting, synthetic data generation, and model training in one Python library. It's for researchers and developers who want to build datasets and use them to fine-tune or align models in reproducible workflows. The library is open source under the MIT license.
Datasets and Synthetic DataLLM Fine-TuningLLM Libraries and SDKsTraining Frameworks+0
An open-source Python library for preparing LLM training data locally or on Slurm and Ray clusters, with filtering, deduplication and generation.
3.4KUpdated 1 day agoApache-2.0
#Batch processing#Distributed execution#Multilingual
DataTrove is an open-source Python library for teams preparing large text datasets, including LLM training corpora. It runs on your own machine or on Slurm and Ray clusters, with processing steps that carry across those environments. It uses the Apache 2.0 license.
Datasets and Synthetic Data+0
A family of AI models you can run offline with Ollama, llama.cpp or LM Studio, with open weights and training data for building specialized agents.
2.1KUpdated 3 weeks agoApache-2.0
Linux#GGUF#Guardrails#Hugging Face integration
Nemotron is NVIDIA's family of AI models for developers building agents that reason, write code and call tools. You can run models locally for private, offline work or deploy them on your own servers. NVIDIA publishes model weights, training data and recipes so teams can inspect and adapt the models for their applications.
Datasets and Synthetic DataEmbedding and Reranker ModelsLLM Fine-TuningOpen-Weight LLMsVision-Language Models+0
A self-hosted vision-language model project for image chat, with a local Gradio interface, GPU inference and Apache 2.0 code.
25KUpdated 2 years agoApache-2.0
macOS · Web#LoRA#Multimodal input#Quantization
LLaVA is a family of vision-language models for researchers and developers who want to ask questions about images on their own hardware. It pairs a CLIP vision encoder with a language model to support image descriptions, visual reasoning and reading text in pictures. Its Python code is open source under Apache 2.0; the project places research-use restrictions on its data and checkpoints, with additional terms from the underlying models.
Datasets and Synthetic DataLLM Fine-TuningSelf-Hosted Chat Web UIsVision-Language Models+0
Python dataset library for AI training and evaluation. Process local files or load data from the Hugging Face Hub, with Apache Arrow storage and streaming.
22KUpdated 2 days agoApache-2.0
#Hugging Face integration#Semantic search
Hugging Face Datasets is an open source Python library for preparing data for AI training and evaluation on your own machine. It's for developers and researchers working with local files or datasets from the Hugging Face Hub. The library runs locally; downloading, streaming or sharing data through the Hub uses Hugging Face's hosted service.
Datasets and Synthetic Data+0
An open-source LLM training and deployment platform under Apache 2.0. Build specialized models on your own infrastructure or use its hosted service.
9.4KUpdated 2 days agoApache-2.0
Docker#Distributed execution#LoRA#Multimodal input
Oumi builds specialized AI models for teams that want control over their training data, model weights, and deployment. Its Apache 2.0 open-source stack runs on laptops, clusters, and your own servers, while its hosted service automates model development from a plain-English task description. You own the resulting weights, data, and training recipes.
Datasets and Synthetic DataLLM Evaluation and TestingLLM Fine-TuningTraining Frameworks+0
An open-source Python library for synthetic data and structured extraction, with Ollama, vLLM, cloud APIs and managed fine-tuning integrations.
1.7KUpdated 2 days agoApache-2.0
#Batch processing#Code execution#Multimodal input
Curator is a Python library for developers preparing LLM training datasets or extracting structured records from existing data. It supports local inference through Ollama and vLLM alongside cloud model APIs, so the same data pipeline can use models on your hardware or a hosted provider. It's open source under Apache 2.0.
Datasets and Synthetic DataLLM Libraries and SDKs+0
An open-source LLM training toolkit that turns documents into specialist datasets, with offline generation on macOS and Linux and optional cloud compute.
1.9KUpdated 3 months agoMIT
macOS · Windows · Linux#Distributed execution#llama.cpp backend#Quantization
Augmentoolkit turns your documents into training data for a custom LLM that learns a particular subject. It's for researchers, developers and hobbyists who want models trained on their own material, such as research papers or fictional lore. The Python toolkit is open source under the MIT license and runs on macOS and Linux, with WSL recommended for Windows.
Data Labeling and AnnotationDatasets and Synthetic DataInference Servers and ClustersLLM Fine-Tuning+0
A self-hosted Python toolkit for curating AI training data across text, images, video and audio, with NVIDIA GPU support and an Apache 2.0 license.
1.8KUpdated 19 hours agoApache-2.0
Linux · Docker#Distributed execution#Hugging Face integration#Multilingual
NeMo Curator is an open source Python toolkit for ML engineers and data teams preparing AI training datasets on their own hardware. It handles text, images, video and audio, with reusable pipelines that can run on a laptop or scale across a multi-node Ray cluster. NVIDIA uses it to prepare data for Nemotron models.
Datasets and Synthetic Data+0
Open-source Python framework for building AI agent teams, generating training data, and simulating agent societies. Licensed under Apache 2.0.
17.8KUpdated 2 weeks agoApache-2.0
#Code execution#Hugging Face integration#Human approval
CAMEL is an open-source Python framework for developers and researchers building systems where AI agents work together. Its focus is on agent roles, communication, and behavior across extended tasks, with applications in synthetic training data, task automation, and simulated societies. It uses the Apache 2.0 license.
Agent FrameworksDatasets and Synthetic Data+0
A Python framework for generating and evaluating LLM datasets, with Apache 2.0 licensing and integrations for Anthropic, Cohere and Argilla.
3.4KUpdated 10 months agoApache-2.0
#Structured output
Distilabel is an open-source Python framework for engineers building datasets to train or evaluate AI models. It pairs synthetic data generation with LLM feedback, so a pipeline can create examples and judge their quality. It uses the Apache 2.0 license.
Data Labeling and AnnotationDatasets and Synthetic DataLLM Evaluation and TestingLLM Libraries and SDKs+0
Open-source AI data processing framework for local machines and Ray clusters, with multimodal cleaning, deduplication and Apache 2.0 licensing.
7.1KUpdated 2 days agoApache-2.0
Docker#Batch processing#Distributed execution#Multimodal input
Data-Juicer is a Python framework for preparing AI datasets on your own machine or a distributed Ray cluster. It's for researchers and teams curating model training data, agent interaction records or documents for retrieval. The project is open source under Apache 2.0.
Data Labeling and AnnotationDatasets and Synthetic DataDocument Parsing for LLMs+0
Open-source LLM evaluation framework in Python with local testing, explainable scores, and support for any LLM judge. Licensed under Apache 2.0.
18.5KUpdated 23 hours agoApache-2.0
#LLM tracing#Multimodal input
DeepEval is a Python framework for testing AI agents, RAG pipelines, and chatbots in your own environment. It's for developers and ML teams who need to compare models or prompts and catch quality regressions before deployment. The open-source framework uses the Apache 2.0 license and fits into Pytest, Python scripts, notebooks, and CI/CD.
Datasets and Synthetic DataLLM Evaluation and TestingLLM Observability and Tracing+0
An open-source Python library for evaluating LLM apps and RAG systems, with custom metrics, test data generation, and LangChain and LlamaIndex integrations.
15.9KUpdated 7 months agoApache-2.0
Ragas is an open-source Python library for developers who need repeatable evaluations of LLM applications and retrieval-augmented generation (RAG) systems. It combines model-based scoring with traditional metrics so teams can compare application changes using test results rather than manual judgments alone. Its license is Apache 2.0.
Datasets and Synthetic DataLLM Evaluation and TestingLLM Libraries and SDKs+0
An open-source AI data annotation tool for human feedback, fine-tuning and evaluation. Run your own server or deploy on Hugging Face Spaces.
5.1KUpdated 1 year agoApache-2.0
Web#Multi-user access#Semantic search
Argilla is an open-source data annotation and feedback tool for AI engineers and domain experts who build training and evaluation datasets. You can run your own Argilla server or deploy it on Hugging Face Spaces. It's licensed under Apache 2.0.
Data Labeling and AnnotationDatasets and Synthetic DataLLM Evaluation and Testing+0
A local AI workbench for macOS, Windows and Linux. Evaluate agents, optimize prompts and run fully offline with Ollama or use cloud APIs.
5.1KUpdated 21 hours ago
macOS · Windows · Linux#Git integration#MCP#Multi-agent workflows
Kiln is a desktop workbench for teams building AI applications on macOS, Windows and Linux. It keeps a task and its dataset together across evaluation, prompt optimization, RAG and fine-tuning, so teams can compare changes against the same examples. Engineers, data scientists, QA staff and subject matter experts can contribute through the app.
Datasets and Synthetic DataLLM Evaluation and TestingLLM Fine-TuningPrompt Management and PlaygroundsRAG Frameworks and Pipelines+0
