JunichiroMorita/llm-jp-3-13b_lora_dpo_kajuma_20241215

Uploaded model

• Developed by: JunichiroMorita
• Language(s) (NLP): Japanese
• License: llama3.1
• Finetuned from model : llm-jp/llm-jp-3-13b

Description

This model was developed for use in a competition, specifically for 松尾研大規模言語モデル講座2024.

Uses

!pip install unsloth
!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 = f"JunichiroMorita/llm-jp-3-13b-it_lora_20241216"

HF_TOKEN = 'your_hugging_face_token'

dtype = None
load_in_4bit = 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\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\n')[-1]

  results.append({"task_id": dt["task_id"], "input": input, "output": prediction})

with open(f'./llm-jp-3-13b-it_lora_20241216_output.jsonl', 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)
        f.write('\n')

Training Details

Training Data

• kajuma/Llama-SFT-3000
• kajuma/dpo_1

Training Procedure

This model was fine-tuned using LoRA (Low-Rank Adaptation) to optimize training efficiency and minimize computational overhead while maintaining performance. The fine-tuning process utilized Japanese instruction data specifically designed for LLMs to enhance its capabilities in understanding and generating Japanese-language instructions.

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.