library_name: transformers
license: cc-by-4.0
datasets:
- elyza/ELYZA-tasks-100
language:
- ja
base_model:
- llm-jp/llm-jp-3-13b
Uses
以下のコードで40分ほどでElyza-tasks-TV-100の推論が終了します。
#推論時のコード
!pip install -U bitsandbytes
!pip install -U transformers
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft
!pip install ipywidgets --upgrade
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
from peft import PeftModel
import torch
from tqdm import tqdm
import json
Hugging Faceで取得したTokenをこちらに貼る。
HF_TOKEN = "YOUR_HF_TOKEN"
model_id = "llm-jp/llm-jp-3-13b"
adapter_id = "kiseich/llm-jp-3-13b-Etask"
QLoRA config
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
Load model
model = AutoModelForCausalLM.from_pretrained( model_id, quantization_config=bnb_config, device_map="auto", token = HF_TOKEN )
Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)
元のモデルにLoRAのアダプタを統合。
model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN) model.eval()
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=512, 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})
import re jsonl_id = re.sub(".*/", "", adapter_id) with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f: for result in results: json.dump(result, f, ensure_ascii=False) # ensure_ascii=False for handling non-ASCII characters f.write('\n')
#以上でjsonlファイルを得る。
Training Data
Elyza-tasks-100にてSFTされている。