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1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "Nexusflow/Athene-V2-Chat"
4
5model = AutoModelForCausalLM.from_pretrained(
6 model_name,
7 torch_dtype="auto",
8 device_map="auto"
9)
10tokenizer = AutoTokenizer.from_pretrained(model_name)
11
12prompt = "Write a Python function to return the nth Fibonacci number in log n runtime."
13
14messages = [
15 {"role": "user", "content": prompt}
16]
17
18text = tokenizer.apply_chat_template(
19 messages,
20 tokenize=False,
21 add_generation_prompt=True
22)
23
24model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
25
26generated_ids = model.generate(
27 **model_inputs,
28 max_new_tokens=2048
29)
30
31generated_ids = [
32 output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
33]
34
35response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]rs in strawberry. For fairness consideration we do not include such system prompt during chat evaluation.