llama3b可运行版本
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@ -40,9 +40,9 @@ Due to the size of the data, you need to download and unzip the data file data.z
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export WANDB_DISABLED=true
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export WANDB_DISABLED=true
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wandb offline
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wandb offline
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CUDA_VISIBLE_DEVICES=0 nohup python finetune_kopa.py \
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CUDA_VISIBLE_DEVICES=0 nohup python finetune_kopa.py \
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--base_model 'YOUR LLM PATH' \
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--base_model 'models/Llama-3.2-3B-Instruct' \
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--data_path 'data/CoDeX-S-train.json' \
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--data_path 'data/CoDeX-S-train.json' \
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--output_dir 'YOUR SAVE PATH' \
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--output_dir 'output' \
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--num_epochs 3 \
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--num_epochs 3 \
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--lora_r 64 \
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--lora_r 64 \
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--learning_rate 3e-4 \
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--learning_rate 3e-4 \
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data/CoDeX-S-rotate.pth
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data/CoDeX-S-rotate.pth
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data/CoDeX-S-test.json
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data/CoDeX-S-test.json
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data/CoDeX-S-train.json
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data/CoDeX-S-train.json
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data/CoDeX-S-valid.json
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data/CoDeX-S-valid.json
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data/FB15K-237N-rotate.pth
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data/FB15K-237N-rotate.pth
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data/FB15K-237N-test.json
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data/FB15K-237N-test.json
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data/FB15K-237N-train.json
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data/FB15K-237N-train.json
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data/FB15K-237N-valid.json
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data/FB15K-237N-valid.json
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@ -21,16 +21,16 @@ from peft import (
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prepare_model_for_int8_training,
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prepare_model_for_int8_training,
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set_peft_model_state_dict,
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set_peft_model_state_dict,
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)
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)
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from transformers import LlamaForCausalLM, LlamaTokenizer
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from transformers import LlamaForCausalLM, AutoTokenizer
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from utils.prompter import Prompter
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from utils.prompter import Prompter
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def train(
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def train(
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# model/data params
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# model/data params
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base_model: str = "", # the only required argument
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base_model = "models/Llama-3.2-3B-Instruct",
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data_path: str = "YOUR LLM PATH",
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data_path: str = "data/CoDeX-S-train.json",
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output_dir: str = "./lora-alpaca",
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output_dir: str = "output",
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# training hyperparams
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# training hyperparams
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batch_size: int = 16,
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batch_size: int = 16,
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micro_batch_size: int = 16,
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micro_batch_size: int = 16,
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@ -110,7 +110,7 @@ def train(
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device_map=device_map,
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device_map=device_map,
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)
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)
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tokenizer = LlamaTokenizer.from_pretrained(base_model)
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tokenizer = AutoTokenizer.from_pretrained(base_model,use_fast=True)
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tokenizer.pad_token_id = (
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tokenizer.pad_token_id = (
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0 # unk. we want this to be different from the eos token
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0 # unk. we want this to be different from the eos token
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4
kopa.py
4
kopa.py
@ -17,7 +17,7 @@ class KoPA(nn.Module):
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self.embeddings = PrefixKGEmbedding(
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self.embeddings = PrefixKGEmbedding(
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num_ent=2034,
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num_ent=2034,
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num_rel=42,
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num_rel=42,
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dim_llm=4096,
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dim_llm=3072,
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num_prefix=1
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num_prefix=1
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)
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)
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@ -73,7 +73,7 @@ class KoPAWithAdapter(nn.Module):
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self.embeddings = PretrainKGEmbedding(
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self.embeddings = PretrainKGEmbedding(
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pretrain_ent_embs=ent_embs,
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pretrain_ent_embs=ent_embs,
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pretrain_rel_embs=rel_embs,
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pretrain_rel_embs=rel_embs,
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dim_llm=4096,
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dim_llm=3072,
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num_prefix=num_prefix
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num_prefix=num_prefix
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)
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)
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else:
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else:
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@ -6,7 +6,6 @@ black
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black[jupyter]
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black[jupyter]
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datasets
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datasets
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fire
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fire
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git+https://github.com/huggingface/peft@smangrul/release-v0.3.0
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transformers>=4.28.0
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transformers>=4.28.0
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sentencepiece
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sentencepiece
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gradio
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gradio
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