diff --git a/rag/llm/cv_model.py b/rag/llm/cv_model.py index 4985cf013..9f26d8044 100644 --- a/rag/llm/cv_model.py +++ b/rag/llm/cv_model.py @@ -49,7 +49,6 @@ class Base(ABC): response = self.client.chat.completions.create( model=self.model_name, messages=history, - max_tokens=gen_conf.get("max_tokens", 1000), temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7) ) @@ -71,7 +70,6 @@ class Base(ABC): response = self.client.chat.completions.create( model=self.model_name, messages=history, - max_tokens=gen_conf.get("max_tokens", 1000), temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7), stream=True @@ -157,8 +155,7 @@ class GptV4(Base): res = self.client.chat.completions.create( model=self.model_name, - messages=prompt, - max_tokens=max_tokens, + messages=prompt ) return res.choices[0].message.content.strip(), res.usage.total_tokens @@ -181,8 +178,7 @@ class AzureGptV4(Base): res = self.client.chat.completions.create( model=self.model_name, - messages=prompt, - max_tokens=max_tokens, + messages=prompt ) return res.choices[0].message.content.strip(), res.usage.total_tokens @@ -241,7 +237,6 @@ class QWenCV(Base): if his["role"] == "user": his["content"] = self.chat_prompt(his["content"], image) response = MultiModalConversation.call(model=self.model_name, messages=history, - max_tokens=gen_conf.get("max_tokens", 1000), temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7)) @@ -271,7 +266,6 @@ class QWenCV(Base): tk_count = 0 try: response = MultiModalConversation.call(model=self.model_name, messages=history, - max_tokens=gen_conf.get("max_tokens", 1000), temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7), stream=True) @@ -306,8 +300,7 @@ class Zhipu4V(Base): res = self.client.chat.completions.create( model=self.model_name, - messages=prompt, - max_tokens=max_tokens, + messages=prompt ) return res.choices[0].message.content.strip(), res.usage.total_tokens @@ -322,7 +315,6 @@ class Zhipu4V(Base): response = self.client.chat.completions.create( model=self.model_name, messages=history, - max_tokens=gen_conf.get("max_tokens", 1000), temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7) ) @@ -344,7 +336,6 @@ class Zhipu4V(Base): response = self.client.chat.completions.create( model=self.model_name, messages=history, - max_tokens=gen_conf.get("max_tokens", 1000), temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7), stream=True @@ -376,12 +367,10 @@ class OllamaCV(Base): def describe(self, image, max_tokens=1024): prompt = self.prompt("") try: - options = {"num_predict": max_tokens} response = self.client.generate( model=self.model_name, prompt=prompt[0]["content"][1]["text"], - images=[image], - options=options + images=[image] ) ans = response["response"].strip() return ans, 128 @@ -399,8 +388,6 @@ class OllamaCV(Base): options = {} if "temperature" in gen_conf: options["temperature"] = gen_conf["temperature"] - if "max_tokens" in gen_conf: - options["num_predict"] = gen_conf["max_tokens"] if "top_p" in gen_conf: options["top_k"] = gen_conf["top_p"] if "presence_penalty" in gen_conf: @@ -429,8 +416,6 @@ class OllamaCV(Base): options = {} if "temperature" in gen_conf: options["temperature"] = gen_conf["temperature"] - if "max_tokens" in gen_conf: - options["num_predict"] = gen_conf["max_tokens"] if "top_p" in gen_conf: options["top_k"] = gen_conf["top_p"] if "presence_penalty" in gen_conf: @@ -480,8 +465,7 @@ class XinferenceCV(Base): res = self.client.chat.completions.create( model=self.model_name, - messages=self.prompt(b64), - max_tokens=max_tokens, + messages=self.prompt(b64) ) return res.choices[0].message.content.strip(), res.usage.total_tokens @@ -497,15 +481,13 @@ class GeminiCV(Base): def describe(self, image, max_tokens=2048): from PIL.Image import open - gen_config = {'max_output_tokens':max_tokens} prompt = "请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。" if self.lang.lower() == "chinese" else \ "Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out." b64 = self.image2base64(image) img = open(BytesIO(base64.b64decode(b64))) input = [prompt,img] res = self.model.generate_content( - input, - generation_config=gen_config, + input ) return res.text,res.usage_metadata.total_token_count @@ -525,7 +507,7 @@ class GeminiCV(Base): history[-1]["parts"].append("data:image/jpeg;base64," + image) response = self.model.generate_content(history, generation_config=GenerationConfig( - max_output_tokens=gen_conf.get("max_tokens", 1000), temperature=gen_conf.get("temperature", 0.3), + temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7))) ans = response.text @@ -551,7 +533,7 @@ class GeminiCV(Base): history[-1]["parts"].append("data:image/jpeg;base64," + image) response = self.model.generate_content(history, generation_config=GenerationConfig( - max_output_tokens=gen_conf.get("max_tokens", 1000), temperature=gen_conf.get("temperature", 0.3), + temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7)), stream=True) for resp in response: @@ -618,8 +600,7 @@ class NvidiaCV(Base): "Authorization": f"Bearer {self.key}", }, json={ - "messages": self.prompt(b64), - "max_tokens": max_tokens, + "messages": self.prompt(b64) }, ) response = response.json()