File size: 1,060 Bytes
4399eba d8690ff 4399eba b009562 4399eba b009562 4399eba b009562 4399eba b009562 |
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 |
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=200):
context = "\n".join(retrieved_texts)
prompt = f"This is the detail about the image:\n{context}\n\nQuestion: {query}\nAnswer:"
result = qwen_pipeline(prompt, max_new_tokens=max_new_tokens, ...)
generated_text = result[0]["generated_text"]
if "Answer:" in generated_text:
final_answer = generated_text.split("Answer:")[-1].strip()
else:
final_answer = generated_text
return final_answer
|