Update README.md

README.md

@@ -1,6 +1,51 @@
 ---
 library_name: peft
+datasets:
+- HachiML/databricks-dolly-15k-ja-for-peft
+language:
+- en
+- ja
 ---
+## JGLUE Score
+We evaluated our model using the following JGLUE tasks. Here are the scores:
+| Task                | Score     |
+|---------------------|----------:|
+| JCOMMONSENSEQA(acc) | 75.78     |
+| JNLI(acc)           | 50.69     |
+| MARC_JA(acc)        | 79.64     |
+| JSQUAD(exact_match) | 62.83     |
+| **Average**         | **67.23** |
+- Note: Use v0.3 prompt template
+- The JGLUE scores were measured using the following script:
+[Stability-AI/lm-evaluation-harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable)
+
+## How to use
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer
+from peft import PeftModel
+
+model_name = "meta-llama/Llama-2-13b-hf"
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+pt_model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+)
+
+peft_name = "HachiML/Llama-2-13b-hf-qlora-dolly-ja-2ep"
+model = PeftModel.from_pretrained(
+    pt_model,
+    peft_name,
+)
+```
+
 ## Training procedure