infer_transformers.py

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
torch.manual_seed(0)

path = 'openbmb/MiniCPM4-8B'
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)

# User can directly use the chat interface
# responds, history = model.chat(tokenizer, "Write an article about Artificial Intelligence.", temperature=0.7, top_p=0.7)
# print(responds)

# User can also use the generate interface
messages = [
    {"role": "user", "content": "Write an article about Artificial Intelligence."},
]
prompt_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([prompt_text], return_tensors="pt").to(device)

model_outputs = model.generate(
    **model_inputs,
    max_new_tokens=1024,
    top_p=0.7,
    temperature=0.7
)
output_token_ids = [
    model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs['input_ids']))
]

responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
print(responses)