llm-jp-3-13b-finetune

Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

Instruction tuning

The models have been fine-tuned on the following datasets.

データセット作成チーム： 関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎. ichikara-instruction: LLMのための日本語インストラクションデータの構築. 言語処理学会第30回年次大会(2024)

Usage

!pip install -U bitsandbytes
!pip install -U transformers
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft
!pip install ipywidgets --upgrade

class CFG:
  HF_TOKEN = "YOUR-HF-TOKEN"
  model_name = "llm-jp/llm-jp-3-13b"
  model_id = "llm-jp/llm-jp-3-13b"
  adapter_id = "ryomy/llm-jp-3-13b-finetune"

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)
from peft import PeftModel
import torch
from tqdm import tqdm
import json

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    CFG.model_id,
    quantization_config=bnb_config,
    device_map="auto",
    token = CFG.HF_TOKEN
)

tokenizer = AutoTokenizer.from_pretrained(CFG.model_id, trust_remote_code=True, token = CFG.HF_TOKEN)

import json
datasets = []
with open(f"./YOUR-DATA.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(".*/", "", CFG.adapter_id)
with open(f"./{CFG.model_name}-{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')

License

Apache License, Version 2.0

Base Model

base_model:- llm-jp/llm-jp-3-13b

---