| """Load the unchanged Qwen base model used by GeoAI Assistant Prototype V1. |
| |
| This repository contains integration documentation, not standalone model weights. |
| """ |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
| BASE_MODEL_ID = "Qwen/Qwen2.5-1.5B-Instruct" |
|
|
|
|
| def main(): |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID) |
| model = AutoModelForCausalLM.from_pretrained( |
| BASE_MODEL_ID, |
| torch_dtype=torch.bfloat16, |
| low_cpu_mem_usage=True, |
| ) |
| model.eval() |
|
|
| messages = [ |
| {"role": "system", "content": "Answer only using the supplied evidence."}, |
| {"role": "user", "content": ( |
| "Evidence: outdoor running is not recommended because air quality " |
| "is unfavorable. Explain the recommendation briefly." |
| )}, |
| ] |
| prompt = tokenizer.apply_chat_template( |
| messages, tokenize=False, add_generation_prompt=True |
| ) |
| inputs = tokenizer(prompt, return_tensors="pt") |
| with torch.inference_mode(): |
| output = model.generate( |
| **inputs, |
| max_new_tokens=80, |
| do_sample=False, |
| pad_token_id=tokenizer.eos_token_id, |
| ) |
| print(tokenizer.decode( |
| output[0][inputs["input_ids"].shape[1]:], |
| skip_special_tokens=True, |
| )) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
