# LLM Domain Adaptation Frameworks

> AI-ranked search results for `continue pretraining on your own domain corpus` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 117 total matches; showing the top 12.

Explore on the web: https://awesome-repositories.com/q/continue-pretraining-on-your-own-domain-corpus

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## Results

- [zyds/transformers-code](https://awesome-repositories.com/repository/zyds-transformers-code.md) (3,782 ⭐) — This project is a collection of scripts and workflows for training, fine-tuning, and deploying large language models using the Hugging Face Transformers toolkit. It functions as a distributed training framework, a library for natural language processing task implementations, and a system for building retrieval-augmented generation chatbots.

The repository includes specialized tools for model optimization, such as a Bayesian hyperparameter optimizer for automatically tuning model settings. It provides implementations for scaling model training across multiple graphics processors using data par
- [liguodongiot/llm-action](https://awesome-repositories.com/repository/liguodongiot-llm-action.md) (23,169 ⭐) — This project is a comprehensive framework for the training, fine-tuning, and deployment of large language models. It functions as a distributed deep learning platform that enables users to scale model workflows across multiple hardware nodes while providing tools for model evaluation and performance benchmarking.

The platform distinguishes itself by offering specialized utilities for model compression and weight transformation, allowing users to reduce memory footprints and latency through quantization and pruning. It supports the adaptation of large models for consumer-grade hardware, facili
- [huggingface/transformers](https://awesome-repositories.com/repository/huggingface-transformers.md) (161,630 ⭐) — Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference.

The library features extensive support for model optimization and
- [qwenlm/qwen3](https://awesome-repositories.com/repository/qwenlm-qwen3.md) (27,324 ⭐) — Qwen3 is a transformer-based large language model designed as a generative AI foundation for understanding, reasoning, and generating human language. It functions as a comprehensive ecosystem for model training, fine-tuning, and production-ready inference, providing the underlying architecture and weights necessary to build diverse artificial intelligence applications.

The project distinguishes itself through extensive support for model quantization and distributed inference, enabling efficient execution across a wide range of hardware from consumer-grade devices to scalable cloud infrastruct
- [nvidia/megatron-lm](https://awesome-repositories.com/repository/nvidia-megatron-lm.md) (16,731 ⭐) — Megatron-LM is a distributed transformer training library and large language model training framework designed to scale models across thousands of GPUs. It functions as a GPU-optimized deep learning toolkit and a scaling engine for mixture-of-experts architectures, enabling the training of models with hundreds of billions of parameters.

The project implements multi-dimensional model parallelism, combining tensor, pipeline, data, expert, and context-based workload distribution. It specifically optimizes mixture-of-experts architectures through integrated memory and communication improvements t
- [facebookresearch/fairseq](https://awesome-repositories.com/repository/facebookresearch-fairseq.md) (32,228 ⭐) — Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning.

The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ
- [jingyaogong/minimind](https://awesome-repositories.com/repository/jingyaogong-minimind.md) (51,834 ⭐) — This project is a comprehensive framework for the entire lifecycle of transformer-based language models, supporting everything from foundational pretraining to specialized deployment. It provides a modular toolkit for defining neural network architectures, managing data preparation pipelines, and executing training routines across various scales. The framework is designed to handle the full model development process, including supervised fine-tuning, behavioral alignment, and the integration of agentic capabilities.

What distinguishes this framework is its focus on efficient training and adva
- [karpathy/nanogpt](https://awesome-repositories.com/repository/karpathy-nanogpt.md) (59,730 ⭐) — nanoGPT is a lightweight engine for training and fine-tuning transformer-based language models from scratch. It provides a minimalist codebase designed for educational exploration and rapid experimentation with neural network architectures, utilizing self-attention and feed-forward layers to process sequences and predict subsequent elements.

The project distinguishes itself through a focus on high-speed data ingestion and hardware-accelerated performance. It includes a dedicated pipeline for transforming raw text into memory-mapped binary files, which enables efficient streaming during traini
- [eleutherai/gpt-neox](https://awesome-repositories.com/repository/eleutherai-gpt-neox.md) (7,392 ⭐) — gpt-neox is a distributed training system and framework for building large-scale autoregressive language models. It implements the transformer architecture and provides a toolkit for training models with billions of parameters by distributing weights across compute clusters.

The framework distinguishes itself through extensive support for distributed model parallelism, including pipeline and sequence parallelism, to overcome single-device memory limits. It further supports sparse model architectures using a mixture of experts system with Sinkhorn-based routing.

The project covers a broad ran
- [shibing624/medicalgpt](https://awesome-repositories.com/repository/shibing624-medicalgpt.md) (4,774 ⭐) — MedicalGPT is an open-source framework for fine-tuning large language models, with a dedicated focus on adapting general models to the medical domain. It provides a complete pipeline that covers continued pretraining on domain-specific corpora, supervised instruction tuning, tokenizer vocabulary extension with medical terminology, and alignment to clinician preferences through direct preference optimization, reinforcement learning, or knowledge distillation. The framework also supports training models to invoke external tools and functions in multi-turn clinical conversations.

The platform di
- [unslothai/unsloth](https://awesome-repositories.com/repository/unslothai-unsloth.md) (66,628 ⭐) — Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade hardware.

The platform distinguishes itself through hand-optimized kernels and automated computational graph techniques that maximize hardware throughput. It supports advanced training methodologies, including reinforcement learning for reasoning and efficient adapter-based fin
- [nvidia/nemo](https://awesome-repositories.com/repository/nvidia-nemo.md) (17,394 ⭐) — NeMo is a multimodal AI framework and toolkit designed for the development, training, and scaling of large language models, generative AI systems, and speech-based models. It functions as an automatic speech recognition toolkit, a text-to-speech engine, and a framework for building models that process and generate combinations of text, image, and audio data.

The project serves as a conversational AI orchestrator capable of managing real-time, interruptible voice interactions. It provides specialized workflows for speech translation, converting spoken audio from one language into text or speec