Update README.md

README.md

@@ -34,6 +34,40 @@ evaluation rubrics, and a score along with the corresponding reasoning.
 - **Repository:** https://github.com/rubricreward/r3
 - **Paper:** https://arxiv.org/abs/2505.13388
 
+## Using the Model
+
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+model_path = "rubricreward/R3-Qwen3-8B-4k"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+sampling_params = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8192, min_p=0, top_k=20)
+
+llm = LLM(
+  model=model_path,
+  dtype="bfloat16",
+  max_model_len=10000,
+  tensor_parallel_size=2,
+  gpu_memory_utilization=0.9,
+  enforce_eager=True,
+)
+
+messages: list[dict[str, str]] = [
+  {'content': "Evaluate the response based on the given task, input, response, and evaluation rubric. Provide a fair and detailed assessment following the rubric...", 'role': 'user'}
+]
+
+list_text = tokenizer.apply_chat_template(
+  messages,
+  tokenize=False,
+  add_generation_prompt=True,
+  enable_thinking=True # Switch between thinking and non-thinking modes. 
+)
+
+outputs = llm.generate(list_text, sampling_params)
+```
+
 ## License and use
 
 R3 is licensed under the Apache 2.0 license.