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大規模言語モデル2024講座（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_unsloth_20241127.ipynbとelyza-tasks-100-TV_0.jsonlを使用して出力します。
model_id = "llm-jp/llm-jp-3-13b"
adapter_id = "Chiaki111/llm-jp-3-13b-it4_lora"
Pythonコード
%%capture !pip install unsloth !pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" !pip install -U torch !pip install -U peft
from unsloth import FastLanguageModel from peft import PeftModel import torch import json from tqdm import tqdm import re
model_id = "llm-jp/llm-jp-3-13b" adapter_id = "Chiaki111/llm-jp-3-13b-it4_lora"
HF_TOKEN = "トークンを記入"
dtype = None # Noneにしておけば自動で設定 load_in_4bit = True # 今回は13Bモデルを扱うためTrue
model, tokenizer = FastLanguageModel.from_pretrained( model_name=model_id, dtype=dtype, load_in_4bit=load_in_4bit, trust_remote_code=True, )
model = PeftModel.from_pretrained(model, adapter_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 = ""
FastLanguageModel.for_inference(model)
results = [] for dt in tqdm(datasets): input = dt["input"]
prompt = f"""### 指示\n{input}\n### 回答\n"""
inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2) prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
json_file_id = "llm-jp-3-13b-it4_lora任意のファイル名"
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')
base_model: llm-jp/llm-jp-3-13b tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en
Uploaded model
- Developed by: Chiaki111
- License: apache-2.0
- Finetuned from model : llm-jp/llm-jp-3-13b
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
