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Memory optimization and training recipes to extrapolate language models' context length to 1 million tokens, with minimal hardware.
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Memory optimization and training recipes to extrapolate language models' context length to 1 million tokens, with minimal hardware.
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This model is finetuned from Llama-2-7B-hf with EasyContext on context length 512K and generalized to 1M tokens. Note that I keep max_position_embeddings in config.json to 4096 because HF llama will create 2D causal mask during initialization. If it is set to 1M GPU will just OOM. You can surely use this model with context length longer than 4096.
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