Add Usage

README.md

@@ -31,6 +31,101 @@ The dataset includes Japanese instruction-response pairs and has been tailored f
 
 ---
 
+## Usage
+
+1. Install Required Libraries
+
+```
+!pip install -U bitsandbytes
+!pip install -U transformers
+!pip install -U accelerate
+!pip install -U datasets
+!pip install -U peft
+```
+
+2. Load the Model and Libraries
+
+```
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
+from peft import PeftModel
+import torch
+
+# Hugging Face Token (recommended to set via environment variable)
+HF_TOKEN = "YOUR_HF_ACCESS_TOKEN"
+
+# Model and adapter IDs
+base_model_id = "llm-jp/llm-jp-3-13b"  # Base model
+adapter_id = "sasakipeter/llm-jp-3-13b-finetune"
+
+# QLoRA (4-bit quantization) configuration
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+```
+
+3. Load the Base Model and LoRA Adapter
+
+```
+# Load base model with 4-bit quantization
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    token=HF_TOKEN
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    base_model_id, 
+    trust_remote_code=True, 
+    token=HF_TOKEN
+)
+
+# Integrate LoRA adapter into the base model
+model = PeftModel.from_pretrained(model, adapter_id, token=HF_TOKEN)
+```
+
+4. Perform Inference
+
+```
+# Example input prompt
+input_text = """次の文章を要約してください。
+
+日本は四季があり、春には桜が咲き、夏には暑さが続きます。秋には紅葉が美しく、冬には雪が降ります。"""
+
+# Format the input prompt
+prompt = f"""### 指示
+{input_text}
+### 回答
+"""
+
+# Tokenize input and move to the model's device
+tokenized_input = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# Generate output
+with torch.no_grad():
+    outputs = model.generate(
+        **tokenized_input,
+        max_new_tokens=100,
+        do_sample=False,
+        repetition_penalty=1.2,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+# Decode the output
+output = tokenizer.decode(outputs[0][tokenized_input.input_ids.size(1):], skip_special_tokens=True)
+print("Output:")
+print(output)
+```
+
+---
+
 ## License
 
 This model is released under the **CC-BY-NC-SA 4.0** license.