Context length plays a crucial role in model performance, as having a larger context improves the behavior and effectiveness of models. Fine-tuning models with extensive context (e.g. 100k tokens) is considered to yield better results compared to smaller context lengths (e.g. 8k). The ability to effectively steer model weights hinges on the amount of context available, highlighting the importance of context length in distinguishing base models from fine-tuned models.

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