from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/ShizhenGPT-7B-LLM",torch_dtype="auto",device_map="auto") tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/ShizhenGPT-7B-LLM") input_text = "为什么我总是手脚冰凉，是阳虚吗？" messages = [{"role": "user", "content": input_text}] inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True ), return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=2048) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor from qwen_vl_utils import process_vision_info processor = AutoProcessor.from_pretrained("FreedomIntelligence/ShizhenGPT-7B-VL") model = Qwen2_5_VLForConditionalGeneration.from_pretrained("FreedomIntelligence/ShizhenGPT-7B-VL", torch_dtype="auto", device_map="auto") messages = [ { "role": "user", "content": [ { "type": "image", "image": "/path/to/your/image.png", }, {"type": "text", "text": "请从中医角度解读这张舌苔。"}, ], } ] text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference: Generation of the output generated_ids = model.generate(**inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text)

from transformers import AutoModelForCausalLM, AutoProcessor from qwen_vl_utils import fetch_image import librosa # Load model and processor model_path = 'FreedomIntelligence/ShizhenGPT-7B-Omni' model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype="auto").cuda() processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) def generate(text, images=None, signals=None): # Process images if provided processed_images = [] if images is not None and images: text = ''.join(['<|vision_start|><|image_pad|><|vision_end|>']*len(images)) + text processed_images = [fetch_image({"type": "image", "image": img, "max_pixels": 360*420}) for img in images if img is not None] else: processed_images = None # Process audio signals if provided processed_signals = [] if signals is not None and signals: text = ''.join(['<|audio_bos|><|AUDIO|><|audio_eos|>']*len(signals)) + text processed_signals = [librosa.load(signal, sr=processor.feature_extractor.sampling_rate)[0] for signal in signals if signal is not None] else: processed_signals = None # Prepare messages messages = [{'role': 'user', 'content': text}] text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) # Ensure text is non-empty if not text: text = [""] # Process the input data input_data = processor( text=[text], audios=processed_signals, images=processed_images, return_tensors="pt", padding=True ) input_data = input_data.to(model.device) # Generate the output generated_ids = model.generate(**input_data, max_new_tokens=1024) generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(input_data.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) return output_text[0] # Example usage # Text input print(generate('为什么我总是手脚冰凉，是阳虚吗？')) # Image input print(generate('请从中医角度解读这张舌苔。', images=['path_to_image'])) # Audio input print(generate('请回答这个语音问题', signals=['path_to_audio']))