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import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat-3.5-0106")
# Single-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
# Multi-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
# Coding Mode
tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids
assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747]1import transformers
2
3tokenizer = transformers.AutoTokenizer.from_pretrained("Nexusflow/Starling-LM-7B-beta")
4model = transformers.AutoModelForCausalLM.from_pretrained("Nexusflow/Starling-LM-7B-beta")
5
6def generate_response(prompt):
7 input_ids = tokenizer(prompt, return_tensors="pt").input_ids
8 outputs = model.generate(
9 input_ids,
10 max_length=256,
11 pad_token_id=tokenizer.pad_token_id,
12 eos_token_id=tokenizer.eos_token_id,
13 )
14 response_ids = outputs[0]
15 response_text = tokenizer.decode(response_ids, skip_special_tokens=True)
16 return response_text
17
18# Single-turn conversation
19prompt = "Hello, how are you?"
20single_turn_prompt = f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant:"
21response_text = generate_response(single_turn_prompt)
22print("Response:", response_text)
23
24## Multi-turn conversation
25prompt = "Hello"
26follow_up_question = "How are you today?"
27response = ""
28multi_turn_prompt = f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: {response}<|end_of_turn|>GPT4 Correct User: {follow_up_question}<|end_of_turn|>GPT4 Correct Assistant:"
29response_text = generate_response(multi_turn_prompt)
30print("Multi-turn conversation response:", response_text)
31
32### Coding conversation
33prompt = "Implement quicksort using C++"
34coding_prompt = f"Code User: {prompt}<|end_of_turn|>Code Assistant:"
35response = generate_response(coding_prompt)
36print("Coding conversation response:", response)@misc{starling2023,
title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF},
url = {},
author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Ganesan, Karthik and Chiang, Wei-Lin and Zhang, Jian and Jiao, Jiantao},
month = {November},
year = {2023}
}