大規模言語モデル講座2024(東京大学松尾・岩澤研究室)で実施しているものです。
Model_Inference_Template_DPO_20241207.ipynbを使用しました。
書き換える箇所
HF_TOKEN = "ご自身のHugging Face Token"
ベースとなるモデルと学習したLoRAのアダプタ。
model_id = "llm-jp/llm-jp-3-13b"
adapter_id = "Chiaki111/llm-jp-3-13b-it2_lora"
adapter_dpo_id = "Chiaki111/llm-jp-3-13b-dpo_2_1"
以上を書き換えたらModel_Inference_Template_DPO_20241207.ipynbを実行してelyza-tasks-100-TV_0.jsonlをアップロードして回答を出します。
'''pythonコード
!pip install -U ipywidgets
!pip install transformers==4.46.3
!pip install -U bitsandbytes
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft==0.13.2
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
from peft import PeftModel
import torch
from tqdm import tqdm
import json
HF_TOKEN ="ご自身のHugging Face Token"
model_id = "llm-jp/llm-jp-3-13b"
adapter_id = "Chiaki111/llm-jp-3-13b-it2_lora"
adapter_dpo_id = "Chiaki111/llm-jp-3-13b-dpo_2_1"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=bnb_config,
device_map="auto",
token = HF_TOKEN
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)
model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
model = PeftModel.from_pretrained(model, adapter_dpo_id, token = HF_TOKEN)
datasets = []
with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
item = ""
for line in f:
line = line.strip()
item += line
if item.endswith("}"):
datasets.append(json.loads(item))
item = ""
results = []
for data in tqdm(datasets):
input = data["input"]
prompt = f"""### 指示
{input}
回答
"""
tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
attention_mask = torch.ones_like(tokenized_input)
with torch.no_grad():
outputs = model.generate(
tokenized_input,
attention_mask=attention_mask,
max_new_tokens=100,
do_sample=False,
repetition_penalty=1.2,
pad_token_id=tokenizer.eos_token_id
)[0]
output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
results.append({"task_id": data["task_id"], "input": input, "output": output})
json_file_id = "llm-jp-3-13b-dpo_2_1_d_m_1"
with open(f"/content/{json_file_id}_output.jsonl", 'w', encoding='utf-8') as f:
for result in results:
json.dump(result, f, ensure_ascii=False)
f.write('\n')
'''
library_name: transformers
tags: []
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