# allenai/olmo

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6,313 stars · 701 forks · Python · apache-2.0

## Links

- GitHub: https://github.com/allenai/OLMo
- Homepage: https://allenai.org/olmo
- awesome-repositories: https://awesome-repositories.com/repository/allenai-olmo.md

## Tags

### Artificial Intelligence & ML

- [Large Language Model Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/large-language-model-training-frameworks.md) — Provides an open-source framework for training, fine-tuning, and running inference on large language models.
- [Checkpointing Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/checkpointing-systems.md) — Releases all intermediate training checkpoints publicly for reproducibility and downstream use.
- [Text Generation Inference Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/pretrained-model-integrations/text-generation-inference-integrations.md) — Runs text generation with pretrained checkpoints using standard tokenization and model APIs.
- [Pretrained Checkpoint Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/model-integration-pipelines/model-inference/pretrained-checkpoint-inference.md) — Loads pretrained checkpoints and generates text responses using standard tokenization and model APIs. ([source](https://cdn.jsdelivr.net/gh/allenai/olmo@main/README.md))
- [Two-Stage Language Model Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/large-language-model-training-frameworks/two-stage-language-model-training-frameworks.md) — Trains large language models from scratch using a two-stage pipeline on web and curated data.
- [Language Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/model-fine-tuning-adaptation/language-model-training.md) — Trains large language models in two stages on web data then curated data, releasing all intermediate checkpoints. ([source](https://cdn.jsdelivr.net/gh/allenai/olmo@main/README.md))
- [8-Bit Inference Quantizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-quantization/8-bit-inference-quantizers.md) — Reduces memory footprint by loading models in 8-bit precision for efficient inference.
- [8-Bit Load-Time Quantizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-quantization/8-bit-load-time-quantizers.md) — Loads models in 8-bit precision with explicit CUDA management for memory-efficient inference. ([source](https://cdn.jsdelivr.net/gh/allenai/olmo@main/README.md))
- [Open Models](https://awesome-repositories.com/f/artificial-intelligence-ml/open-models.md) — Releases an open-source language model with publicly available training data and intermediate checkpoints.
- [Pretrained Checkpoint Loaders](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-model-components/pretrained-checkpoint-loaders.md) — Provides a standard PyTorch interface for loading pretrained checkpoints and generating text.
- [BitsAndBytes Quantizers](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization/quantized-model-implementations/bitsandbytes-quantizers.md) — Provides 8-bit quantization via bitsandbytes for memory-efficient inference on CUDA devices.
- [Text Generation Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/text-generation-interfaces.md) — Provides an interface for loading pretrained checkpoints and generating text using standard APIs.
- [HuggingFace Transformers Loaders](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-inference-engines/huggingface-transformers-loaders.md) — Integrates with HuggingFace Transformers for model loading, tokenization, and inference workflows.
- [CUDA Tensor Placement Managers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/tensor-computing-libraries/tensor-memory-management/cuda-tensor-placement-managers.md) — Manages CUDA tensor placement and memory explicitly for optimized inference performance.
- [Model Quantization Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/serving-and-runtime/model-quantization-tools.md) — Loads language models in 8-bit precision to lower memory usage during inference on CUDA devices.

### Software Engineering & Architecture

- [Two-Stage](https://awesome-repositories.com/f/software-engineering-architecture/training-pipelines/two-stage.md) — Implements a two-stage training pipeline on web data then curated data with checkpoint release.