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  DerekZZ 2024-11-05 12:16

  4.0

  Interesting:4,Helpfulness:4,Website Frontend:3,Correctness:4

  Prompt: how to implement a sandbox environment for python with docker and container

  I asked ChatGPT (GPT4o) question about python coding "sandbox python programs with Docker environment". The responses are helpful and the generated code samples are clear. But the website front end is having a URL rendering issue of the "Docker installation guide". The font is highlighted so it means it's a link. But there is no href or URL associated with the text and I can't find the installation guide it refers to. Is it a known problem? Reproducing links: https://chatgpt.com/share/67298cb0-47c0-8005-96ea-0562df9f7158

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  codemonkey 2024-10-31 17:12

  4.0

  Interesting:4,Helpfulness:4,Correctness:4

  Prompt: Implement the function of transformer model, users typed some lines such as "def transformer(x):"

  Another more complicated task I tested on Cursor Code Editor is to write a python code to implement the Transformer function as in paper "Attention is all you need". The code I am writing include function name "def transformer(x):" and some of the beginning lines like "q=torch.linear(". And with the suggested code, I wrap up the transformer function in less than one minutes. So it can really help me increase the productivity of writing code. The only drawback is when I want to modify the code and use backspace to delete some lines, it seems like Cursor doesn't know what to do and give no hints for a while. Unless I start writing something else. And the newly suggested code will usually be quite different from the previously suggested ones. ``` ## Machine Learning ### Input import torch def transformer(x): ndim = 16 q=torch.linear(ndim, ndim)(x) k=torch.linear(ndim, ndim)(x) v=torch.linear(ndim, ndim)(x) attn = torch.matmul(q, k.transpose(-2, -1)) attn = attn / math.sqrt(ndim) attn = torch.softmax(attn, dim=-1) ### Cursor Suggested Code: out = torch.matmul(attn, v) out = torch.linear(ndim, ndim)(out) return out ```

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  codemonkey 2024-10-31 17:05

  5.0

  Interesting:5,Helpfulness:5,Succinct:5,Correctness:5

  Prompt: write a python coding function of calculating fabonacci series

  I just downloaded Cursor on Mac and tried some coding task. The first task is to write a fabonacci function in python. And once I typed the name "fabonacci(n)" and end the function name with the colon ":", there is a code hint tab poping-up suggesting the remaining code. The suggested codes are correct and are very succinct. Very Helpful AI coding tools. ``` def fabonacci(n): if n == 0: return 0 elif n == 1: return 1 else: return fabonacci(n-1) + fabonacci(n-2) ```

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  ChenYZ 2024-10-29 12:08

  5.0

  Interesting:4,Helpfulness:5,Correctness:5

  Prompt: how to export prompt and completion data in json format and convert to parquet format

  I asked ChatGPT (GPT 4o) a coding related questions "export prompt and completion data in json format and convert to parquet format", and the python code generated by ChatGPT are quite good. It gives me examples of detailed json data format, python code to convert raw json data to Parquet format and even recommends all the python libs requirements. Very helpful response from ChatGPT(GPT4o) !!! Responses from ChatGPT: Step 1: Export Data in JSON Format Step 2: Convert JSON to Parquet using Python Explanation of the Code Prerequisites. And the ChatGPT dialogue sharing link is here: https://chatgpt.com/share/67205caa-a068-8005-be6b-76eeed480fbe

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  xiaolei98 2024-09-13 12:18

  5.0

  Interesting:5,Helpfulness:5,Correctness:5

  Prompt: Implement LLM LLaMa Architecture in python code using pyTorch library, Then use distilling techniques to distill a large LLaMa model (large than 70B) to a small student model, with size limit to 2B. Please think step by step and provide details of the model code.

  I asked the OpenAI o1 model to implement the LLaMa Architecture LLM in python code using pytorch with a distill function. The overall response is excellent. It breaks down the tasks into a few steps, including : 1. Set Up Your Environment 2. Implement the LLaMa Architecture 3. Prepare the Distillation Process And as for the code it self, it consists of a few sections, including: Load the large LLaMa model and tokenizer. Prepare a smaller student model for distillation. Define a custom distillation loss function. Create a custom dataset for training. Set up a trainer with the distillation loss function. Train the student model using the teacher model. I actually examined the distill loss coding, which is the KL Divergence between the student logits and the teacher logits. The results are correct. """ loss = nn.functional.kl_div(student_probs, teacher_probs, reduction='batchmean') """

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