Update README.md

README.md

@@ -17,8 +17,47 @@ Lingma SWE-GPT has demonstrated impressive performance in software engineering t
 - Outperforms other open-source models of similar scale in software engineering-specific tasks.
 
 ## How to use
+
+### Run on SWE-bench 
 Refer to https://github.com/LingmaTongyi/Lingma-SWE-GPT
 
+### Quick Start
+```
+from modelscope import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Lingma/Lingma-SWE-GPT-7B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are Lingma, created by Tongyi Lingma team. You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
 ## TODO
 Currently only Python is supported. In future updates, we will provide more support for Java, JS/TS and other languages.