ChatTS-8B

from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
import torch
import numpy as np

hf_model = "bytedance-research/ChatTS-14B"
# Load the model, tokenizer and processor
# For pre-Ampere GPUs (like V100) use `_attn_implementation='eager'`
model = AutoModelForCausalLM.from_pretrained(hf_model, trust_remote_code=True, device_map="auto", torch_dtype='float16')
tokenizer = AutoTokenizer.from_pretrained(hf_model, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(hf_model, 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
You are a helpful assistant.<|im_end|><|im_start|>user
{prompt}<|im_end|><|im_start|>assistant
"""
# 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))