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from transformers import AutoModelForCausalLM, AutoTokenizer
import os
model_name = os.path.join(os.path.dirname(__file__), "Qwen25llm")
print("Loading Qwen2.5 files...")
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
) # load model
tokenizer = AutoTokenizer.from_pretrained(model_name)
def describe(prompt, system_prompt):
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}
] # construct msgs
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
) # get text
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
) # generate
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
] # generate
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response
def discriminate(class_name, prompt):
system_prompt = "You are an accurate discriminator. " \
"You need to determines if the class name matches the description. " \
"Answer with YES or NO."
keywords = [word for word in prompt.split(" ")
if "select" in word or "classif" in word or "find" in word or "all" in word]
if len(keywords) == 0:
description = prompt
else: # # len(keywords > 0)
description = prompt.rsplit(keywords[-1], 1)[-1]
prompt = f"Does the {class_name} belong to \"{description}\"? \n\nAnswer me with YES or NO."
result = describe(prompt, system_prompt)
if "NO" in result or "no" in result or "No" in result:
return False
else: # assert YES in result
return True
def get_embedding(prompt):
class_names = ("airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck")
results = []
for class_name in class_names:
result = discriminate(class_name, prompt)
results.append(result)
return results
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