-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest.py
73 lines (51 loc) · 2.04 KB
/
test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
"""
Run inference on a test sample for the new fine-tuned model
"""
from unsloth import FastLanguageModel, is_bfloat16_supported
from transformers import TrainingArguments, TextStreamer
from peft import PeftModel
import pandas as pd
SAVED_MODEL_FOLDER = "model_2024-10-15_09-49-14"
SAVED_ADAPTER_FOLDER = "output/checkpoint-24"
def main(filename: str = "math_dataset_test.pkl"):
df_test = pd.read_pickle(filename)
model, tokenizer = load_model(with_lora=True)
responses = generate_responses(model, tokenizer, df_test)
df_test["response_finetuned_model"] = responses
model, tokenizer = load_model(with_lora=False)
responses = generate_responses(model, tokenizer, df_test)
df_test["response_non_finetuned_model"] = responses
now = pd.Timestamp.now().strftime("%Y-%m-%d_%H-%M-%S")
df_test.to_pickle(filename.replace('.pkl', f'_final_{now}.pkl'))
print(df_test.head())
def load_model(with_lora: bool = True):
max_seq_length = 1024
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=SAVED_MODEL_FOLDER,
max_seq_length=max_seq_length,
load_in_4bit=True,
dtype=None,
)
model = FastLanguageModel.for_inference(model)
if with_lora:
model = PeftModel.from_pretrained(model, SAVED_ADAPTER_FOLDER)
return model, tokenizer
def generate_responses(model, tokenizer, df):
messages = df["prompt"].tolist()
responses = []
for message in messages:
# need to wrap in a list
message = [message]
inputs = tokenizer.apply_chat_template(
message,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to("cuda")
text_streamer = TextStreamer(tokenizer)
response = model.generate(input_ids=inputs, streamer=text_streamer, max_new_tokens=64, use_cache=True)
response_txt = tokenizer.decode(response[0], skip_special_tokens=True)
responses.append(response_txt)
return responses
if __name__ == "__main__":
main("math_dataset_test.pkl")