Add README

README.md

@@ -1,69 +1,87 @@
-# LLM-JP-3.3.7B-IT LoRA Model
+---
+language: ja
+tags:
+  - text-generation
+  - japanese
+  - llm
+  - lora
+  - instruction-tuning
+license: cc-by-nc-sa-4.0
+datasets:
+  - ichikara-instruction
+  - Ego/jpflan-raw
+base_model: llm-jp/llm-jp-3-3.7b
+model_name: llm-jp-3-3.7b-it_lora_all
+widget:
+  - text: ""
+---
 
-This is the LLM-JP-3.3.7B-IT model fine-tuned with LoRA.
+# LLM-JP-3.3.7B LoRA Model
 
-# How to Use
+This is the **LLM-JP-3.3.7B** model fine-tuned with LoRA for instruction-based Japanese text generation tasks. The model has been fine-tuned on datasets **ichikara-instruction** and **Ego/jpflan-raw**.
+
+## How to Use
+
+Below is an example of how to use the model for inference:
 
 ```python
-##################################################
 import torch
 from unsloth import FastLanguageModel
 from peft import PeftModel
 
-HF_TOKEN = ""
+HF_TOKEN = ""  # Add your Hugging Face token here
 
-# モデルとトークナイザーの読み込み
+# Load the base model and tokenizer
 model, tokenizer = FastLanguageModel.from_pretrained(
     model_name="llm-jp/llm-jp-3-3.7b",
     dtype=None,
     load_in_4bit=True,
     trust_remote_code=True,
 )
-model = PeftModel.from_pretrained(model, "nito78/llm-jp-3-3.7b-it_lora_all", token = HF_TOKEN)
+model = PeftModel.from_pretrained(model, "nito78/llm-jp-3-3.7b-it_lora_all", token=HF_TOKEN)
 
-# 推論モード
+# Switch to inference mode
 FastLanguageModel.for_inference(model)
-##################################################
+
+# Example usage
 import json
 
-# データの読み込み
+# Load dataset
 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 = ""
-##################################################
+        line = line.strip()
+        item += line
+        if item.endswith("}"):
+            datasets.append(json.loads(item))
+            item = ""
+
 from tqdm import tqdm
 
-# 推論
+# Perform inference
 results = []
 for dt in tqdm(datasets):
-  input = dt["input"]
+    input = dt["input"]
+
+    prompt = f"""### 指示\n{input}\n### 回答\n"""
 
-  prompt = f"""### 指示\n{input}\n### 回答\n"""
+    inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
 
-  inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True, do_sample=False, repetition_penalty=1.2)
+    prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split("\n### 回答")[-1]
 
-  outputs = model.generate(**inputs, max_new_tokens = 512, 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})
 
-  results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
-##################################################
-import re
 import os
 import json
 
-# 保存先ディレクトリ
+# Save results
 output_dir = "./results"
 os.makedirs(output_dir, exist_ok=True)
 
-# 結果を保存
 output_file = os.path.join(output_dir, "result.jsonl")
-with open(output_file, 'w', encoding='utf-8') as f:
+with open(output_file, "w", encoding="utf-8") as f:
     for result in results:
         json.dump(result, f, ensure_ascii=False)
-        f.write('\n')
+        f.write("\n")