# LLM Synthetic Data Generation

> Search results for `generate synthetic training data with LLMs` on awesome-repositories.com. 115 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/generate-synthetic-training-data-with-llms

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## Results

- [camel-ai/camel](https://awesome-repositories.com/repository/camel-ai-camel.md) (17,253 ⭐) — This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer.

The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva
- [dair-ai/prompt-engineering-guide](https://awesome-repositories.com/repository/dair-ai-prompt-engineering-guide.md) (75,678 ⭐) — This project is a comprehensive educational resource and technical guide focused on the development, optimization, and application of large language models. It provides a structured curriculum for mastering prompt engineering, ranging from foundational principles of instruction design to advanced techniques for improving model reasoning, accuracy, and reliability.

The guide distinguishes itself by offering deep technical insights into agentic workflows and autonomous system design. It covers the implementation of multi-step reasoning chains, tool integration through function calling, and stat
- [googlecloudplatform/training-data-analyst](https://awesome-repositories.com/repository/googlecloudplatform-training-data-analyst.md) (8,566 ⭐) — This project is a cloud data analysis sandbox and a collection of courseware designed for learning data analysis techniques on Google Cloud Platform. It serves as a training lab containing technical demonstrations and practical exercises for skill development and cloud certification.

The repository provides guided labs and demonstrations focused on Google Cloud data analysis, encompassing technical training for the platform's specific data services. It enables the practice of cloud data engineering and the use of big data tooling to perform queries and data transformations.

The environment s
- [datajuicer/data-juicer](https://awesome-repositories.com/repository/datajuicer-data-juicer.md) (6,574 ⭐) — Data-Juicer is an open-source framework for cleaning, filtering, deduplicating, and transforming multimodal datasets to prepare them for training large language and vision models. It functions as a distributed data pipeline engine that runs processing jobs across Ray clusters, handling billions of samples with automatic operator fusion and adaptive parallelism. The framework provides a library of operators that leverage large language models for semantic extraction, filtering, and data synthesis within processing pipelines.

The project distinguishes itself through a YAML-based data recipe sys
- [wiseodd/generative-models](https://awesome-repositories.com/repository/wiseodd-generative-models.md) (7,497 ⭐) — This is a generative AI model library containing a collection of PyTorch and TensorFlow implementations for creating synthetic data and modeling complex probability distributions. It serves as a multi-framework repository of deep learning models designed for learning and replicating data patterns.

The project provides specialized implementation suites for several generative architectures. This includes Generative Adversarial Networks using competing generator and discriminator models, Variational Autoencoder frameworks that map data to a latent space, and Restricted Boltzmann Machine and Deep
- [ydataai/ydata-synthetic](https://awesome-repositories.com/repository/ydataai-ydata-synthetic.md) (1,642 ⭐) — Synthetic data generators for tabular and time-series data
- [vibrantlabsai/ragas](https://awesome-repositories.com/repository/vibrantlabsai-ragas.md) (12,659 ⭐) — Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications.

The framework distinguishes itself through its ability to generate synthetic test datasets from existin
- [gretelai/gretel-synthetics](https://awesome-repositories.com/repository/gretelai-gretel-synthetics.md) (679 ⭐) — Synthetic data generators for structured and unstructured text, featuring differentially private learning.
- [comet-ml/opik](https://awesome-repositories.com/repository/comet-ml-opik.md) (17,787 ⭐) — Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It provides a centralized environment for tracing execution flows, managing prompt templates, and monitoring production performance, allowing teams to gain visibility into complex model interactions and tool usage without requiring manual application code changes.

The platform distinguishes itself through its integrated approach to the AI development lifecycle, combining distributed trace instrumentation with automated evaluation frameworks. It supports model-as-a-judge scoring, syn
- [tatsu-lab/stanford_alpaca](https://awesome-repositories.com/repository/tatsu-lab-stanford-alpaca.md) (30,266 ⭐) — This project provides an end-to-end framework for adapting large language models to follow user instructions through supervised fine-tuning. It functions as a comprehensive training pipeline that enables the creation of specialized assistant models by minimizing the difference between predicted outputs and target responses within structured instruction datasets.

The framework distinguishes itself by integrating synthetic data generation with memory-efficient training techniques. It utilizes powerful language models to iteratively expand small sets of human-written seeds into diverse, high-qua
- [d2l-ai/d2l-en](https://awesome-repositories.com/repository/d2l-ai-d2l-en.md) (29,001 ⭐) — This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation.

The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
- [shizhediao/post-training-data-flywheel](https://awesome-repositories.com/repository/shizhediao-post-training-data-flywheel.md) (65 ⭐) — We aim to provide the best references to search, select, and synthesize high-quality and large-quantity data for post-training your LLMs.
- [jakevdp/pythondatasciencehandbook](https://awesome-repositories.com/repository/jakevdp-pythondatasciencehandbook.md) (48,561 ⭐) — This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping.

The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st
- [eleutherai/gpt-neo](https://awesome-repositories.com/repository/eleutherai-gpt-neo.md) (8,275 ⭐) — GPT-Neo is an open-source distributed training framework designed for scaling GPT-2 and GPT-3-style language models across multiple devices using mesh-tensorflow for model parallelism. It provides the infrastructure to train transformer-based language models with billions of parameters across distributed computing environments, making large-scale language model research accessible outside of proprietary systems.

The framework supports training both autoregressive GPT-style models and masked language models like BERT or RoBERTa, with configurable masking strategies and token handling. It inclu
- [xamey/deploy-llms-with-ansible](https://awesome-repositories.com/repository/xamey-deploy-llms-with-ansible.md) (3 ⭐) — Easily deploy LLMs with Ansible. Uses Docker with llama.cpp or ollama. Secured with whitelisted IPs.
- [conardli/easy-dataset](https://awesome-repositories.com/repository/conardli-easy-dataset.md) (13,394 ⭐) — Easy-dataset is a comprehensive platform designed for the end-to-end management of machine learning datasets, specifically tailored for language and vision model fine-tuning. It functions as a centralized environment for the entire data lifecycle, encompassing the automated generation of synthetic training data, the structural organization of document collections, and the systematic annotation of individual data points.

The platform distinguishes itself through its integrated evaluation and orchestration capabilities. It provides a dedicated suite for benchmarking models, featuring blind side
- [aishwaryanr/awesome-generative-ai-guide](https://awesome-repositories.com/repository/aishwaryanr-awesome-generative-ai-guide.md) (24,755 ⭐) — This project is a community-driven knowledge repository and technical learning resource focused on the field of generative artificial intelligence. It serves as a centralized hub for developers and practitioners to access curated research, tutorials, and foundational concepts necessary for building and deploying modern artificial intelligence applications.

The platform distinguishes itself through a collaborative, distributed contribution model that aggregates diverse learning materials into a structured, searchable knowledge base. It covers a wide range of specialized topics, including retri
- [confident-ai/deepeval](https://awesome-repositories.com/repository/confident-ai-deepeval.md) (13,733 ⭐) — Deepeval is a framework for testing and evaluating large language model applications. It provides a suite of tools for executing automated regression tests, validating model output quality against defined standards, and tracing the execution of complex agent workflows. By integrating these capabilities into development pipelines, the platform ensures consistent performance and reliability throughout the software lifecycle.

The platform distinguishes itself through its focus on programmatic validation and observability. It utilizes secondary language models to score output quality and employs
- [drizzle-team/drizzle-orm](https://awesome-repositories.com/repository/drizzle-team-drizzle-orm.md) (34,835 ⭐) — Drizzle ORM is a TypeScript-native database toolkit providing type-safe SQL query building, schema management, and automated migrations across PostgreSQL, MySQL, SQLite, and SingleStore.
- [trusthlt/private-synthetic-text-generation](https://awesome-repositories.com/repository/trusthlt-private-synthetic-text-generation.md) (4 ⭐) — This repository contains the source code to replicate the experimental results in our paper.
- [grafana/grafana](https://awesome-repositories.com/repository/grafana-grafana.md) (74,456 ⭐) — Grafana is an observability data platform designed to aggregate metrics, logs, and traces from diverse sources into a unified environment. It functions as a centralized interface for visualizing complex telemetry data, transforming raw streams into interactive dashboards that support real-time system health tracking and performance monitoring.

The platform distinguishes itself through a plugin-based modular architecture that integrates disparate databases, cloud services, and monitoring tools via a standardized data abstraction layer. This framework allows for the dynamic loading of external
- [microsoft/unilm](https://awesome-repositories.com/repository/microsoft-unilm.md) (22,030 ⭐) — This project is a comprehensive framework and toolkit for developing, optimizing, and deploying transformer-based models across multimodal, document intelligence, and natural language processing tasks. It provides a unified neural architecture that processes text, vision, audio, and document layout data through a shared set of weights, enabling researchers and developers to build foundational models that align cross-modal representations.

The platform distinguishes itself through advanced training and inference strategies designed for large-scale deep learning. It incorporates specialized mec
- [diyago/tabular-data-generation](https://awesome-repositories.com/repository/diyago-tabular-data-generation.md) (570 ⭐) — We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.
- [crewaiinc/crewai](https://awesome-repositories.com/repository/crewaiinc-crewai.md) (53,687 ⭐) — CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations.

The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo
- [zhongyy/unequal-training-for-deep-face-recognition-with-long-tailed-noisy-data](https://awesome-repositories.com/repository/zhongyy-unequal-training-for-deep-face-recognition-with-long-tailed-noisy-data.md) (0 ⭐) — This is the code of CVPR 2019 paper《Unequal Training for Deep Face Recognition with Long Tailed Noisy Data》.
- [gokumohandas/made-with-ml](https://awesome-repositories.com/repository/gokumohandas-made-with-ml.md) (48,343 ⭐) — Made-With-ML is an automated documentation generator and developer experience platform designed to transform source code into structured, searchable reference websites. It functions as a codebase intelligence tool that parses implementation details to provide clear explanations of logic and data requirements.

The system distinguishes itself by leveraging language-level type annotations and structured code comments to generate interface specifications. By utilizing static analysis to extract metadata, it automates the transformation of docstrings into web-ready documentation, ensuring that tec
- [zju-llms/foundations-of-llms](https://awesome-repositories.com/repository/zju-llms-foundations-of-llms.md) (15,771 ⭐) — Foundations-of-LLMs is an educational curriculum and technical resource designed to explain the mathematical and computational principles behind modern generative language models. It provides a structured guide for developers and practitioners to master the fundamental concepts, architectural designs, and training methodologies that enable these systems to function.

The project covers the core mechanisms of transformer-based sequence modeling, including self-attention, subword tokenization, and autoregressive generation. It details the technical frameworks used in natural language processing
- [data-creative/next-train-api](https://awesome-repositories.com/repository/data-creative-next-train-api.md) (0 ⭐) — The Next Train API provides a JSON web service for any GTFS feed. Deploy this source code to your own Heroku server to set up an API for your own agency's feed. Let me know how it goes. I'm happy to support you!
- [bididi-badidi/fyp-data-analysis-with-llm](https://awesome-repositories.com/repository/bididi-badidi-fyp-data-analysis-with-llm.md) (10 ⭐) — Human interpretation of data is inherently susceptible to cognitive biases. While Large Language Models (LLMs) act as automated data analysts, they often mirror user biases or training artifacts. This project introduces a "Bias-Contrastive" Agentic Framework that goes beyond simple text analysis.
- [limix-ldm-ai/limix](https://awesome-repositories.com/repository/limix-ldm-ai-limix.md) (3,538 ⭐) — LimiX is a tabular foundation model and a suite of tools for structured data, providing a transformer-based system for classification, regression, and data generation. It includes a causal inference engine to determine cause-and-effect relationships, a synthetic data generator, and a framework for filling missing dataset values through feature context prediction.

The project optimizes tabular inference through a high-performance system that uses ensemble-based sample retrieval to increase prediction speed and accuracy on high-specification hardware. It further distinguishes itself by using tr
- [facebook/react](https://awesome-repositories.com/repository/facebook-react.md) (245,669 ⭐) — React is a JavaScript library for building user interfaces based on a component-driven architecture and unidirectional data flow.
- [ujjwalkarn/data-mining-with-r](https://awesome-repositories.com/repository/ujjwalkarn-data-mining-with-r.md) (6 ⭐) — This is the notes of data mining with r. please refer to: http://www.liaad.up.pt/~ltorgo/DataMiningWithR Thanks goes to the author. 20111203
- [packtpublishing/llm-engineers-handbook](https://awesome-repositories.com/repository/packtpublishing-llm-engineers-handbook.md) (4,774 ⭐) — This project is an educational resource and engineering guide for building, deploying, and optimizing large language model applications and production pipelines. It serves as a blueprint for cloud AI infrastructure, providing a framework for orchestrating inference endpoints, data warehouses, and scalable production environments.

The repository provides specific implementation patterns for retrieval augmented generation to ground model responses in external data. It includes a training workflow for crawling, structuring, and processing datasets to facilitate model fine-tuning, alongside an ev
- [microsoft/airsim](https://awesome-repositories.com/repository/microsoft-airsim.md) (17,956 ⭐) — AirSim is a high-fidelity simulation platform designed for the development and testing of autonomous vehicles. Built as a plugin for game engines, it provides a physics-based environment that models vehicle dynamics and sensor data, serving as a foundation for robotics research, computer vision training, and reinforcement learning.

The platform distinguishes itself through its support for hardware-in-the-loop and software-in-the-loop testing, allowing developers to validate control logic and firmware against real-world signals or concurrent processes. It offers extensive programmatic control
- [facebookresearch/map-anything](https://awesome-repositories.com/repository/facebookresearch-map-anything.md) (2,915 ⭐) — Map-anything is a 3D scene reconstruction framework and neural geometry estimator designed to transform two-dimensional images into metric three-dimensional spatial representations using feed-forward neural networks. It provides a specialized toolkit for predicting camera intrinsics and ray directions from single images without requiring external geometric metadata.

The project includes a 3D model benchmarking suite that utilizes a unified model wrapper to standardize outputs from diverse reconstruction models. This allows for consistent evaluation and accuracy measurement across various spat
- [raaminz/training](https://awesome-repositories.com/repository/raaminz-training.md) (28 ⭐) — This Repository is all about my training classes
- [rednaga/training](https://awesome-repositories.com/repository/rednaga-training.md) (431 ⭐) — Training materials crafted and publicly provided by Red Naga members
- [e2b-dev/awesome-ai-agents](https://awesome-repositories.com/repository/e2b-dev-awesome-ai-agents.md) (25,903 ⭐) — This project is a curated repository and directory focused on the artificial intelligence agent ecosystem. It serves as a centralized knowledge base for developers and researchers to discover frameworks, platforms, and autonomous software entities designed for reasoning, planning, and executing complex tasks.

The directory distinguishes itself through a community-driven curation model, where contributors maintain and update the collection via a distributed version control system. This collaborative approach ensures that the index remains current with the latest academic resources, open-source
- [opendcai/dataflow](https://awesome-repositories.com/repository/opendcai-dataflow.md) (2,926 ⭐) — DataFlow is an agent-based workflow orchestrator and data pipeline designed to synthesize, clean, and augment large-scale datasets for training large language models. It functions as a synthetic data generator and text curation tool, utilizing an intelligent assistant to assemble modular processing operators into functional pipelines based on user requirements.

The project distinguishes itself through a low-code approach, providing a web-based visual interface for designing and monitoring multi-stage execution flows. It features an operator-based registry system that allows for the integratio
- [svc-develop-team/so-vits-svc](https://awesome-repositories.com/repository/svc-develop-team-so-vits-svc.md) (28,097 ⭐) — This project is a singing voice conversion tool based on VITS generative modeling. It transforms the identity of a singing voice to a target speaker while preserving the original melody, lyrics, and intonation.

The system distinguishes itself through hybrid voice synthesis, allowing for the blending of multiple speaker identities via linear model interpolation. It utilizes cluster-based feature retrieval to increase target voice similarity and employs a diffusion probabilistic model as a post-processor to remove electronic artifacts and improve vocal clarity.

The software covers a broad rang
- [eugeneyan/open-llms](https://awesome-repositories.com/repository/eugeneyan-open-llms.md) (12,804 ⭐) — 📋 A list of open LLMs available for commercial use.
- [emqx/emqx](https://awesome-repositories.com/repository/emqx-emqx.md) (16,422 ⭐) — This project is a high-performance MQTT broker and IoT data platform designed to manage millions of concurrent device connections. It provides a scalable infrastructure for ingesting, processing, and routing telemetry data across distributed systems, utilizing an actor-based concurrency model to maintain high availability and state synchronization across cluster nodes.

The platform distinguishes itself through integrated stream processing and edge computing capabilities. It allows users to execute declarative SQL-based rules directly against incoming message streams for real-time filtering, t
- [answerdotai/llms-txt](https://awesome-repositories.com/repository/answerdotai-llms-txt.md) (2,442 ⭐) — The /llms.txt file, helping language models use your website
- [bitwarden/server](https://awesome-repositories.com/repository/bitwarden-server.md) (18,074 ⭐) — This project provides a comprehensive, self-hosted platform for zero-knowledge credential management and enterprise secrets orchestration. It functions as a secure vault that ensures all encryption and decryption processes occur exclusively on the client side, preventing the server from ever accessing plaintext data. By combining identity federation with robust access controls, the system enables organizations to centralize the management of passwords, passkeys, and sensitive infrastructure credentials.

The platform distinguishes itself through its focus on both human-centric security and aut
- [nvlabs/ffhq-dataset](https://awesome-repositories.com/repository/nvlabs-ffhq-dataset.md) (4,099 ⭐) — This project provides a high-resolution face dataset consisting of 70,000 human face images in PNG format. It serves as a curated library of aligned images and facial landmark data designed for generative model training, facial recognition, and image synthesis research.

The dataset includes machine-readable metadata that pairs images with precise facial coordinate points, source URLs, and copyright information. This coordinate data enables the transformation of raw photos into a standardized 1024x1024 pixel resolution through landmark-based alignment and cropping.

The repository includes aut
- [zero-9215/online-cl-llms](https://awesome-repositories.com/repository/zero-9215-online-cl-llms.md) (3 ⭐) — Generate the training script by executing:
- [clickhouse/clickhouse](https://awesome-repositories.com/repository/clickhouse-clickhouse.md) (48,229 ⭐) — ClickHouse is a high-performance, columnar analytical database designed for real-time query execution and large-scale data aggregation. It functions as a distributed data warehouse capable of processing petabytes of information, while also providing an embedded engine that integrates directly into applications for native query capabilities without external dependencies. The system is built to handle high-throughput ingestion and complex analytical workloads, delivering millisecond-level latency for interactive dashboards and operational monitoring.

The platform distinguishes itself through ad
- [thudm/slime](https://awesome-repositories.com/repository/thudm-slime.md) (4,259 ⭐) — SLIME is a distributed reinforcement learning framework for large language model post-training that bridges Megatron training with SGLang inference servers. It orchestrates scalable RL loops across GPU clusters, decoupling training and inference into independent processes that communicate over HTTP and NCCL for independent scaling and fault tolerance. The system supports multi-agent reinforcement learning workflows with parallel agent instances, customizable rollout strategies, and personalized agent serving that improves models from prior conversations without disrupting API serving.

The fra
- [daytonaio/daytona](https://awesome-repositories.com/repository/daytonaio-daytona.md) (72,416 ⭐) — Daytona is a cloud-native development environment platform designed to orchestrate ephemeral, containerized workspaces. It provides a centralized system for managing reproducible coding environments as code, ensuring consistency across distributed teams by abstracting the underlying infrastructure. By utilizing declarative configuration, the platform automates the entire lifecycle of development sandboxes, from initial provisioning to resource governance.

The platform distinguishes itself through its infrastructure-agnostic runner layer, which allows development environments to be deployed ac
- [screenpipe/screenpipe](https://awesome-repositories.com/repository/screenpipe-screenpipe.md) (16,932 ⭐) — Screenpipe is a local-first platform designed to record, index, and analyze desktop activity. By capturing screen, audio, and keyboard input, it creates a comprehensive and searchable history of computer usage. The system functions as an activity recorder and automation framework, providing a persistent, context-aware memory that allows artificial intelligence agents to observe and interact with local desktop environments.

The platform distinguishes itself through a privacy-focused architecture that processes all data locally. It utilizes on-device computer vision and speech recognition to tr