Upload README.md

Add example usage.

README.md

@@ -6,7 +6,29 @@ Here is an example of a ChatTS application, which allows users to interact with
 ![Chat](figures/chat_example.png)
 
 ## Usage
-This model is fine-tuned on the QWen2.5-14B-Instruct (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) model. For more usage details, please refer to the `README.md` in the ChatTS repository.
+- This model is fine-tuned on the QWen2.5-14B-Instruct (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) model. For more usage details, please refer to the `README.md` in the ChatTS repository.
+- An example usage of ChatTS (with `HuggingFace`):
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
+import torch
+import numpy as np
+
+# Load the model, tokenizer and processor
+model = AutoModelForCausalLM.from_pretrained("./ckpt", trust_remote_code=True, device_map=0, torch_dtype='float16')
+tokenizer = AutoTokenizer.from_pretrained("./ckpt", trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("./ckpt", trust_remote_code=True, tokenizer=tokenizer)
+# Create time series and prompts
+timeseries = np.sin(np.arange(256) / 10) * 5.0
+timeseries[100:] -= 10.0
+prompt = f"I have a time series length of 256: <ts><ts/>. Please analyze the local changes in this time series."
+# Apply Chat Template
+prompt = f"<|im_start|>system\nYou are a helpful assistant.<|im_end|><|im_start|>user\n{prompt}<|im_end|><|im_start|>assistant\n"
+# Convert to tensor
+inputs = processor(text=[prompt], timeseries=[timeseries], padding=True, return_tensors="pt")
+# Model Generate
+outputs = model.generate(**inputs, max_new_tokens=300)
+print(tokenizer.decode(outputs[0][len(inputs['input_ids'][0]):], skip_special_tokens=True))
+```
 
 ## Reference
 - QWen2.5-14B-Instruct (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)