# openaccess-ai-collective/axolotl

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12,062 stars · 1,370 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/OpenAccess-AI-Collective/axolotl
- Homepage: https://docs.axolotl.ai
- awesome-repositories: https://awesome-repositories.com/repository/openaccess-ai-collective-axolotl.md

## Description

Axolotl is a distributed training orchestrator and fine-tuning framework for large language models, multimodal systems, and quantized models. It provides a structured environment for specializing pre-trained models through full parameter updates or low-rank adaptation, as well as aligning model outputs with human expectations via preference tuning pipelines and reward modeling.

The system distinguishes itself through a configuration-driven pipeline that manages preprocessing and training workflows via a single file for reproducibility. It implements high-throughput optimizations such as multipacking sequence processing and distributed tensor parallelism to scale workloads across multiple GPUs and hardware nodes.

The framework covers broad capability areas including memory optimization through quantization and reduced-precision fine-tuning, sharded data distribution for large datasets, and specialized training workflows for vision and audio models. It further supports human-aligned behavior tuning using reinforcement learning from human feedback.

## Tags

### Artificial Intelligence & ML

- [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training.md) — Scales model training across multiple GPUs and hardware nodes using distributed parallelization strategies.
- [Cross-Hardware Workload Distribution](https://awesome-repositories.com/f/artificial-intelligence-ml/cross-hardware-workload-distribution.md) — Distributes training workloads across multiple GPUs and hardware nodes to enable parallel execution of large models. ([source](https://github.com/openaccess-ai-collective/axolotl#readme))
- [Reward Modeling](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/reward-modeling.md) — Provides reward modeling capabilities to evaluate and score text generation for human preference alignment.
- [Preference Alignment](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/reward-modeling/preference-alignment.md) — Implements optimization loops that use reward scores to align model outputs with human preferences.
- [Distributed Training Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-orchestrators.md) — Orchestrates distributed training workloads across multiple GPUs and nodes using advanced parallelism.
- [GPU Resource Scaling](https://awesome-repositories.com/f/artificial-intelligence-ml/gpu-resource-scaling.md) — Implements GPU resource scaling through distributed tensor and sequence parallelism to handle large-scale model workloads. ([source](https://github.com/openaccess-ai-collective/axolotl#readme))
- [LLM Fine-Tuning Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/training-systems/model-training-engines/llm-fine-tuning-engines.md) — Serves as a specialized engine for the efficient fine-tuning of large language models on custom datasets.
- [Large Language Model Fine-Tuning Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/integrated-development-platforms/machine-learning-platforms/large-language-model-fine-tuning-frameworks.md) — Provides a specialized framework for adapting pre-trained large language models to specific tasks and datasets.
- [Preference-Based Model Alignments](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/fine-tuning-and-alignment/preference-based-model-alignments.md) — Refines model behavior using preference tuning and reward modeling to align outputs with human expectations.
- [Language Model Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/language-model-fine-tuning.md) — Implements specialized workflows for adapting pre-trained language models to specific tasks or datasets. ([source](https://github.com/openaccess-ai-collective/axolotl#readme))
- [Low-Rank Adaptation](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/low-rank-adaptation.md) — Supports Low-Rank Adaptation (LoRA) for memory-efficient fine-tuning by updating only a small set of weights.
- [Memory Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/memory-optimization.md) — Reduces VRAM requirements during training through quantization and reduced-precision fine-tuning.
- [Tensor Parallelism](https://awesome-repositories.com/f/artificial-intelligence-ml/tensor-parallelism.md) — Implements tensor parallelism to partition model weights across multiple GPUs for handling large-scale models.
- [Multimodal Model Trainers](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-trainers/multimodal-training-interfaces/multimodal-model-trainers.md) — Provides an environment for optimizing vision and audio models to process diverse media formats.
- [Quantization-Aware Training](https://awesome-repositories.com/f/artificial-intelligence-ml/model-quantization/quantization-aware-training.md) — Maintains model accuracy while reducing weight precision by integrating quantization directly into the training process. ([source](https://github.com/openaccess-ai-collective/axolotl#readme))
- [Multimodal Training](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-training.md) — Provides techniques and workflows for training models across multiple data modalities, including vision and audio. ([source](https://github.com/openaccess-ai-collective/axolotl#readme))
- [Quantized Training](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization/quantized-training.md) — Integrates weight precision reduction into the training system to lower VRAM and memory usage.
- [Multipacking](https://awesome-repositories.com/f/artificial-intelligence-ml/text-sequence-processing/sequence-length-constraints/multipacking.md) — Implements multipacking to group short training examples into fixed-length blocks for maximized GPU throughput.
- [Training Configuration Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/training-configuration-systems.md) — Uses a unified configuration system to externalize hyperparameters and define reproducible training workflows. ([source](https://github.com/openaccess-ai-collective/axolotl#readme))
- [Training Throughput Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/training-throughput-optimization.md) — Improves training efficiency and reduces compute time using multipacking, optimized attention mechanisms, and specialized kernels. ([source](https://github.com/openaccess-ai-collective/axolotl#readme))

### Data & Databases

- [Training Memory Optimizers](https://awesome-repositories.com/f/data-databases/memory-optimization-strategies/training-memory-optimizers.md) — Lowers VRAM requirements during training through quantization, low-rank adaptations, and reduced-precision fine-tuning. ([source](https://github.com/openaccess-ai-collective/axolotl#readme))
- [Distributed Sharding Architectures](https://awesome-repositories.com/f/data-databases/database-management-systems/database-architectures/distributed-sharding-architectures.md) — Partitions large training datasets across multiple compute nodes to enable parallel batch processing.

### Development Tools & Productivity

- [Configuration-Driven Orchestrators](https://awesome-repositories.com/f/development-tools-productivity/configuration-driven-orchestrators.md) — Provides a declarative configuration system to manage complex preprocessing and training workflows for reproducibility.

### Part of an Awesome List

- [Deep Learning Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/deep-learning-frameworks.md) — Streamlines fine-tuning configurations for various AI model architectures.