OLMo 2: Fully Open-Source Foundation Model

Ai2's groundbreaking OLMo 2 language models are fully open-source, setting a new benchmark for performance and transparency in the field of large language models (LLMs). These autoregressive models boast optimized training, innovative data mixtures, and advanced instruction tuning techniques. Let's delve into the details.

"Everyone wants open-source language models, but no one wants to lift these heavy ass weights." - Nathan Lambert (@natolambert)

This tweet perfectly encapsulates the challenge Ai2 has overcome. Their "2 OLMo 2 Furious" paper details their success.

Table of Contents

• 2 OLMo 2 Furious: A Deep Dive
• Key Features of OLMo 2
  • Robust Training Stability
  • Optimized Data Blends
  • Architectural Enhancements
• Post-Training Refinements
• Infrastructure: A Key Ingredient
• OLMo 2 Benchmarked: Performance Compared
• Experiencing OLMo 2
• Accessing OLMo 2: Key Links
• Conclusion

2 OLMo 2 Furious: A Deep Dive

OLMo 2, available in 7B and 13B parameter sizes, distinguishes itself through complete transparency. Ai2 has publicly released training data, code, recipes, and even intermediate checkpoints, fostering collaboration and accelerating research. These models deliver performance comparable to industry leaders like Llama 3.1 and Qwen 2.5, but with significantly improved efficiency.

The "2 OLMo 2 Furious" research paper provides comprehensive details.

Key Features of OLMo 2

Robust Training Stability

OLMo 2 tackles common training instabilities (loss spikes) using:

• Data Refinement: Filtering redundant n-grams.
• Improved Initialization: A standardized initialization scheme.
• Regularization: Employing z-loss to stabilize output logits.

These improvements enable smoother training and efficient handling of larger datasets.

Optimized Data Blends

OLMo 2 employs a two-stage pretraining approach:

• Initial Pretraining: Leveraging 5 trillion tokens of high-quality web data.
• Mid-Training Enhancement: Integrating domain-specific datasets (math, STEM), exemplified by the Dolmino Mix 1124 dataset.

Architectural Enhancements

OLMo 2's architecture incorporates:

• RMSNorm: For stable activation normalization.
• Reordered Layer Norm: Enhancing stability by normalizing attention and feedforward layer outputs.
• High-Resolution Positional Encoding: Rotary positional embeddings with increased resolution.

These architectural choices contribute to scalability and efficiency.

Post-Training Refinements

OLMo 2's post-training leverages the Tülu 3 recipe, focusing on:

• Supervised Fine-Tuning (SFT): Refining instruction-following abilities.
• Reinforcement Learning with Verifiable Rewards (RLVR): Optimizing performance on specific tasks (math, factual reasoning).

This results in OLMo 2-Instruct models excelling in benchmarks like GSM8K and MMLU.

Infrastructure: A Key Ingredient

Ai2's advanced infrastructure is crucial to OLMo 2's success:

• High-Performance Computing Clusters: Utilizing NVIDIA H100 GPUs across multiple data centers.
• Beaker Workload Management: For efficient workload distribution and monitoring.

This robust infrastructure minimizes training interruptions and maximizes resource utilization.

OLMo 2 Benchmarked: Performance Compared

OLMo 2 frequently outperforms Qwen 2.5 and Llama 3.1 on specific tasks, particularly with the inclusion of Dolmino Mix 1124. It also demonstrates remarkable efficiency, achieving comparable or superior results with up to 20% fewer FLOPs.

Experiencing OLMo 2

Access the model and try it yourself! Instructions for local use are also available.

Accessing OLMo 2: Key Links

• Paper: https://www.php.cn/link/cb14acf78723becd7023f4f56027cece
• Blog: https://www.php.cn/link/96b0548661234c39ac2a02872f8cfcb2
• Demo: https://www.php.cn/link/3eebaed369eb3ae36a90f310fc33638c
• Collection: https://www.php.cn/link/ae3b166c302150f4def9a8176fd36460

Conclusion

OLMo 2 represents a significant advancement in open-source AI, prioritizing transparency and innovation. By openly sharing its resources, Ai2 fosters collaboration and accelerates progress in the field, driving the future of AI applications.

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