kky84176/gemma-2-9b-it-it01

Uploaded model

• Developed by: kky84176
• License: apache-2.0
• Finetuned from model : unsloth/gemma-2-9b-it-bnb-4bit

This gemma2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Sample Use

以下は、elyza-tasks-100-TV_0.jsonlの回答のためのコードです。

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

HF_TOKEN = "your-token"
model_name = "kky84176/gemma-2-9b-it-it01"

#
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4", # nf4は通常のINT4より精度が高く、ニューラルネットワークの分布に最適です
    bnb_4bit_compute_dtype=torch.bfloat16,
)

# モデルの読込み
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto",
    token=HF_TOKEN,
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remoe_code=True, token=HF_TOKEN)

# データの読込み
import json
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 = ""

# モデルによる推論
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})

# jsonl への出力
import re
new_model_id = "gemma-2-9b-it-it01"
jsonl_id = re.sub(".*/", "", new_model_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')