instruction-gen/

Qwen2.5-VL-3B-3k-Zero-GRPO-NIG-lora-format-meteor-llm

def construct_messages(image_root, x):
    QUESTION_TEMPLATE = "{Question} First output the thinking process in <think> </think> tags and then output the final answer in <answer> </answer> tags. Output the final answer in string format."
    image_path = os.path.join(image_root, x['image'])
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": f"file://{image_path}"
                },
                {
                    "type": "text",
                    "text": QUESTION_TEMPLATE.format(Question=x['problem'])
                }
            ]
        }]
    return messages


class QWEN_VL_Inference():
    def __init__(self, base_model_path, adapter_path):
        self.base_model_path = base_model_path
        self.adapter_path = adapter_path
        self.processor = AutoProcessor.from_pretrained(self.base_model_path)
        self.load_merge_model()

    def load_merge_model(self):
        self.base_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
            self.base_model_path,
            torch_dtype=torch.bfloat16,
            attn_implementation="flash_attention_2",
            device_map="auto"
        )
        self.model = PeftModel.from_pretrained(self.base_model, self.adapter_path)
        self.merged_model = self.model.merge_and_unload()

    def infer(self, messages):
        text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        image_inputs, _ = process_vision_info(messages)
        inputs = self.processor(text=[text], images=image_inputs, return_tensors="pt", padding=True).to('cuda')
        generated_ids = self.merged_model.generate(**inputs, max_new_tokens=512)
        generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
        output_text = self.processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True,clean_up_tokenization_spaces=False)
        return output_text