update readme

README.md

@@ -30,4 +30,97 @@ The models have been fine-tuned on the following datasets.
 
 
 ### Usage
-TODO
+
+```terminal
+pip install -U bitsandbytes
+pip install -U transformers
+pip install -U accelerate
+pip install -U datasets
+pip install -U peft
+```
+
+```python
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
+from peft import PeftModel
+import torch
+from tqdm import tqdm
+import json
+
+HF_TOKEN = "your_hf_token"
+model_id = "llm-jp/llm-jp-3-13b" 
+adapter_id = "hachimada/llm-jp-3-13b-finetune-v0"
+```
+
+```python
+# QLoRA config
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    token = HF_TOKEN
+)
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)
+# Apply adapter
+model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
+```
+
+
+```python
+# Load your dataset
+datasets = []
+with open("./your_dataset.jsonl", "r") as f:
+    item = ""
+    for line in f:
+      line = line.strip()
+      item += line
+      if item.endswith("}"):
+        datasets.append(json.loads(item))
+        item = ""
+```
+
+```python
+# Inference
+results = []
+for data in tqdm(datasets):
+
+  input = data["input"]
+
+  prompt = f"""### 指示
+  {input}
+  ### 回答
+  """
+
+  tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
+  attention_mask = torch.ones_like(tokenized_input)
+  with torch.no_grad():
+      outputs = model.generate(
+          tokenized_input,
+          attention_mask=attention_mask,
+          max_new_tokens=512,
+          do_sample=False,
+          repetition_penalty=1.2,
+          pad_token_id=tokenizer.eos_token_id
+      )[0]
+  output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
+
+  results.append({"task_id": data["task_id"], "input": input, "output": output})
+
+# dump to jsonl
+import re
+jsonl_id = re.sub(".*/", "", adapter_id)
+with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
+    for result in results:
+        json.dump(result, f, ensure_ascii=False)  # ensure_ascii=False for handling non-ASCII characters
+        f.write('\n')
+```