Update README.md

README.md

@@ -1,6 +1,46 @@
 ---
 library_name: peft
 ---
+
+## Model Usage
+
+```python
+import torch
+import transformers
+from finetune_peft import get_peft_config, PEFTArguments
+from peft import get_peft_model
+
+model_path = 'EleutherAI/pythia-6.9b-deduped'
+# peft_path = 'models/codegen25_7b/checkpoint'
+peft_path = '0xk1h0/pythia-6.9b-deduped-py150k-r20-LoRA'
+# peft_path = 'models/alpaca-llama-7b-peft/params.p'
+
+torch.set_default_tensor_type(torch.cuda.HalfTensor)
+model = transformers.AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, cache_dir='models')
+peft_config = get_peft_config(peft_args=PEFTArguments(peft_mode="lora"))
+model = get_peft_model(model, peft_config)
+# model.load_state_dict(torch.load(peft_path), strict=False)
+torch.set_default_tensor_type(torch.cuda.FloatTensor)
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+batch = tokenizer("""
+### Generate AES MODE encrypt function.
+""", return_tensors="pt")
+
+with torch.no_grad():
+    out = model.generate(
+        input_ids=batch["input_ids"],
+        attention_mask=torch.ones_like(batch["input_ids"]),
+        max_length=256,
+        do_sample=True,
+        temperature = 0.4,
+        top_p=0.95
+        
+    )
+print(tokenizer.decode(out[0]))
+```
+
+
 ## Training procedure