Update README.md

README.md

@@ -42,6 +42,45 @@ SPARK is a novel reinforcement learning framework that enables autonomous strate
 | ScienceWorld L2 | **49.2%** | 33.6% | 30.5% |
 | WebShop | **75.8%** | 29.7% | 32.0% |
 
+## Quickstart
+Here we provide a transformers inference style:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Jinyang23/Spark-1.5B-ScienceWorld"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Calculate the sum of 123 and 456. Provide only the numerical answer."
+
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. 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)
+```
+
 ## Citation
 
 If you use this model or the SPARK framework in your research, please cite: