%%capture
!pip install unsloth
!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
from unsloth import FastLanguageModel
import torch
import json

from unsloth import FastLanguageModel
import torch
import json
from unsloth import FastLanguageModel
import torch
import json

max_seq_length = 2048
dtype = None
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = model_name,
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
    token = HF_TOKEN,
)
FastLanguageModel.for_inference(model)


# データセットの読み込み。
# omnicampusの開発環境では、左にタスクのjsonlをドラッグアンドドロップしてから実行。
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 = ""

!pip install triton
!pip install torch --upgrade --index-url https://download.pytorch.org/whl/cu118
!pip install torchinductor --upgrade --index-url https://download.pytorch.org/whl/cu118

from tqdm import tqdm
import torch # Import torch explicitly

# 推論
# gemma
results = []
for data in tqdm(datasets):

  input = data["input"]
  prompt = f"""### 指示
  {input}
  ### 回答：
  """

  with torch.no_grad():  # Disable gradient calculation during inference for efficiency
    input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**input_ids, max_new_tokens=512, do_sample=False, repetition_penalty=1.2)
    output = tokenizer.decode(outputs[0][input_ids.input_ids.size(1):], skip_special_tokens=True)

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

with open(f"/content/gemma2-9b-finetune-1_output.jsonl", 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)
        f.write('\n')

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gemma2-9b-finetune-1