Update README.md

README.md

@@ -199,4 +199,62 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 [More Information Needed]
 ### Framework versions
 
-- PEFT 0.13.2
+- PEFT 0.13.2
+
+以下は、elyza-tasks-100-TV_0.jsonlの推論のためのコードです。
+
+- %%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
+
+model_name = "XAT928/llm-jp-3-13b-finetune-elyza"
+
+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("/content/sample_data/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 = ""
+
+from tqdm import tqdm
+
+# 推論
+results = []
+for dt in tqdm(datasets):
+  input = dt["input"]
+
+  prompt = f"""### 指示\n{input}\n### 注意\n簡潔に回答してください。\n### 回答\n"""
+
+  inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
+
+  outputs = model.generate(**inputs, max_new_tokens = 1024, 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}) 
+
+with open(f"/content/{model_name}_output.jsonl", 'w', encoding='utf-8') as f:
+    for result in results:
+        json.dump(result, f, ensure_ascii=False)
+        f.write('\n')