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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
# Replace with your target Qwen model on Hugging Face
MODEL_NAME = "Qwen/Qwen2.5-7B-Instruct"
# Initialize tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
device_map="auto", # or "cuda", etc. if you want to specify
trust_remote_code=True
)
# Create pipeline
qwen_pipeline = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer
)
def generate_response(retrieved_texts, query, max_new_tokens=500):
"""
Generates a response based on the retrieved texts and query using Qwen.
Args:
retrieved_texts (list): List of retrieved text strings (e.g., from BLIP).
query (str): The user's question about the image.
max_new_tokens (int): Maximum tokens to generate for the answer.
Returns:
str: The generated answer.
"""
# Construct a prompt that includes the image details as context
context = "\n".join(retrieved_texts)
prompt = f"This is the detail about the image:\n{context}\n\nQuestion: {query}\nAnswer:"
# Generate the text
result = qwen_pipeline(
prompt,
max_new_tokens=max_new_tokens,
do_sample=True, # or False if you want deterministic output
temperature=0.7, # tweak as needed
)
# The pipeline returns a list of dicts with key "generated_text"
full_generation = result[0]["generated_text"]
# Optionally parse out the final answer if the model repeats the prompt
if "Answer:" in full_generation:
final_answer = full_generation.split("Answer:")[-1].strip()
else:
final_answer = full_generation
return final_answer
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