Postpone decisions, try to avoid direct questions, and leave room for exploration:由于模型可能会出错,最好避免不可逆的决策,并为使用不同可能的解决方案进行探索和代码迭代留出空间, 具体来说, 类似于生成模型的解码方法, 不要选用top 1 greedy decoing, 要选用beam sampling, 例如在 Initial code solution部分...
save_quantized(quantized_model_dir, use_safetensors=True) #load quantized model to the first GPU model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir, device="cuda:0", trust_remote_code=True) #inference with model.generate print(tokenizer.decode(model.generate(**tokenizer("auto_gpt...
torch_dtype=torch.float16,load_in_4bit=True,trust_remote_code=True,device_map="auto",)whileTrue:prompt=input("Enter Prompt: ")input_ids=tokenizer(prompt,return_tensors="pt").input_ids.to("cuda")gen_tokens=model.generate(input_ids,do_sample=True,max_length=100)generated_...
Step10:Continue to improve upon all aspects mentioned above by following trendsinweb design and staying up-to-date onnewtechnologiesthat can enhance user experience even further!How does a Website Work?Awebsite works by having pages,which are madeofHTMLcode.This code tells your computer how to...
"function": "CodeGenerator", "description": "Generates python code for a described problem", "arguments": [ { "name": "prompt", "type": "string", "description": "description of the problem for which the code needs to be generate" ...
(12)SELFEVOLVE: A Code Evolution Framework via Large Language Models较早的Code LLM自进化,一方面让LLM根据问题生成知识,另一方面接受解释器等的反馈来迭代(13)CORRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding在推理端用代码强化故事理解能力(14)Natural Language to Code: How Far ...
使用分隔符清楚地指示输入的不同部分(Use delimiters to clearly indicate distinct parts of the input) 使用分隔符的意义在于避免用户输入的文本可能存在一些误导性的话语对应用功能造成干扰,下面是一个提示注入的例子: 分隔符是以下任意一个: ```, """, < >, ...
def append_to_history(self, user_prompt, response): self.history.append((user_prompt, response)) if len(self.history) > self.history_length: self.history.pop(0) 最后,我们实现了generate函数,该函数根据输入提示生成文本。 每个LLM都有一个用于培训的特定提示模板。对于Code Llama,我使用了codellama-...
#load quantized model to the first GPU model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir, device="cuda:0", trust_remote_code=True) #inference with model.generate print(tokenizer.decode(model.generate(**tokenizer("auto_gptq is", return_tensors="pt").to(model.device))[0]))...
Write Python code to compute the square root and print the result。 # To find the square root of a number in Python, you can use the math library and its sqrt function: frommathimportsqrt number=float(input('Enter a number: ')) ...