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matatonic
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,48 @@ | ||
| from transformers import AutoTokenizer, AutoModelForCausalLM | ||
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| from vision_qna import * | ||
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| # THUDM/cogvlm2-llama3-chat-19B | ||
| # THUDM/cogvlm2-llama3-chinese-chat-19B | ||
| import transformers | ||
| transformers.logging.set_verbosity_error() | ||
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| class VisionQnA(VisionQnABase): | ||
| model_name: str = "cogvlm2" | ||
| format: str = 'llama3' | ||
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| def __init__(self, model_id: str, device: str, device_map: str = 'auto', extra_params = {}, format = None): | ||
| super().__init__(model_id, device, device_map, extra_params, format) | ||
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| self.tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=self.params.get('trust_remote_code', False)) | ||
| self.model = AutoModelForCausalLM.from_pretrained(**self.params).eval() | ||
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| print(f"Loaded on device: {self.model.device} with dtype: {self.model.dtype}") | ||
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| async def chat_with_images(self, request: ImageChatRequest) -> str: | ||
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| query, history, images, system_message = await prompt_history_images_system_from_messages( | ||
| request.messages, img_tok='', url_handler=url_to_image) | ||
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| input_by_model = self.model.build_conversation_input_ids(self.tokenizer, query=query, history=history, images=images, template_version='chat') | ||
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| inputs = { | ||
| 'input_ids': input_by_model['input_ids'].unsqueeze(0).to(self.model.device), | ||
| 'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(self.model.device), | ||
| 'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(self.model.device), | ||
| 'images': [[input_by_model['images'][0].to(self.model.device).to(self.model.dtype)]] if images else None, | ||
| } | ||
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| default_params = { | ||
| 'max_new_tokens': 2048, | ||
| 'pad_token_id': 128002, | ||
| 'top_p': None, # 0.9 | ||
| 'temperature': None, # 0.6 | ||
| } | ||
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| params = self.get_generation_params(request, default_params) | ||
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| response = self.model.generate(**inputs, **params) | ||
| answer = self.tokenizer.decode(response[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True).strip() | ||
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| return answer |
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