Update README.md

README.md

@@ -90,7 +90,7 @@ FastLanguageModel.for_inference(model)
 results = []
 for dt in tqdm(datasets):
     input = dt["input"]
-    prompt = f"""### 指示\n{input}\n### 回答\n"""
+    prompt = f"""以下は、タスクを説明する指示です。要求を適切に満たす回答を書きなさい。### 指示\n{input}\n### 回答\n"""
     # 推論処理
 ```
 
@@ -112,4 +112,53 @@ JSONLファイルとして保存
     "input": "入力テキスト",
     "output": "モデルの出力"
 }
+```
+
+```
+model_id = "llm-jp/llm-jp-3-13b"
+adapter_id = "154teru/llm-jp-3-13b-it15a4_fullset2048_lora"
+
+HF_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("/content/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"""以下は、タスクを説明する指示です。要求を適切に満たす回答を書きなさい。### 指示\n{input}\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("/content/prompt1024.jsonl", 'w', encoding='utf-8') as f:
+    for result in results:
+        json.dump(result, f, ensure_ascii=False)
+        f.write('\n')
 ```