Fast and memory-efficient exact attention. Contribute to Dao-AILab/flash-attention development by creating an account on GitHub.
Fast and memory-efficient exact attention. Contribute to Dao-AILab/flash-attention development by creating an account on GitHub.
rotary_emb(value_states, position_ids) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) past_key_value = getattr(self, "past_key_value", past_key_value) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_...
serve train llama_flash_attn_monkey_patch.py llama_xformers_attn_monkey_patch.py train.py train_baichuan.py train_flant5.py train_lora.py train_lora_t5.py train_mem.py train_xformers.py __init__.py constants.py conversation.py
We read every piece of feedback, and take your input very seriously. Include my email address so I can be contacted Cancel Submit feedback Saved searches Use saved searches to filter your results more quickly Cancel Create saved search Sign in Sign up Reseting focus {...