Update README.md

README.md

@@ -8,8 +8,50 @@ language:
 We fine-tuned our ChemGPT2-QA-72B based on the Qwen2-72B-Instruct model. Our training data, ChemGPT-2.0-Data, has been open-sourced and is available at https://huggingface.co/datasets/ALmonster/ChemGPT-2.0-Data.
 We evaluated our model on the three chemistry tasks of C-Eval and compared it with GPT-3.5 and GPT-4. The results are as follows:
 
-| C-Eval | college_chemistry | high_school_chemistry | middle_school_chemistry | AVG |
+
+## C-Eval
+
+| Models | college_chemistry | high_school_chemistry | middle_school_chemistry | AVG |
 |--------|-------------------|-----------------------|-------------------------|-----|
 | GPT-3.5 | 0.397             | 0.529                 | 0.714                   | 0.54666667 |
 | GPT4   | 0.594             | 0.558                 | 0.811                   | 0.65433333 |
-| chemgpt| 0.71              | 0.936                 | 0.995                   | 0.88033333 |
+| chemgpt| 0.71              | 0.936                 | 0.995                   | 0.88033333 |
+
+
+## Quickstart
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "ALmonster/ChemGPT2-QA-72B",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("ALmonster/ChemGPT2-QA-72B")
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "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(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    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]
+```