Update README.md

README.md

@@ -8,6 +8,10 @@ base_model: meta-llama/Meta-Llama-3-8B-Instruct
 
 We are thrilled to present Llama3-KALE-LM-Chem 8B, the newest version of our Llama3-KALE-LM-Chem model, which embodies nearly half a year of innovation.
 
+## Training Details
+
+We have continue pre-trained the model with a large amount of data and post-trained it using supervised fine-tuning.
+
 ## Benchmarks
 
 ### Open Benchmarks
@@ -36,3 +40,43 @@ We are thrilled to present Llama3-KALE-LM-Chem 8B, the newest version of our Lla
 | ChemLLM-7B-Chat-1.5-SFT | 50.06 | 49.51 | 85.28 | 38.75 | 38.00 | 26.67 | 28.33 | 31.68 | 33.67 | 42.44 |
 | OURMODEL | 63.58 | 58.39 | 92.98 | 44.50 | 48.67 | 38.33 | 46.33 | 44.55 | 34.33 | 52.41 |
 | OURMODELINSTRUCT | 61.33 | 43.44 | 90.30 | 53.62 | 72.67 | 53.67 | 46.00 | 47.03 | 45.00 | 57.01 |
+
+## Quick Start 
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-8B",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-8B")
+
+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=2048
+)
+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]
+```
+
+## Citation
+
+Will Coming soon....