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') """

I used the OpenAI o1 preview model to implement the frontend code of login and logout function of H5 mobile application and separate css, html and js code into separate files. The model's response to the front end code generation task is very helpful. And I actually copy and paste the code into a separate folder and tried it myself. The website front end is shown in the attached images. It is working to some extend, except that the CSS file is a little bit strange. The o1 model generates the code and also gives these explanations, including: index.html: Contains the structure of the login and logout pages. styles.css: Provides the styling for the pages to make them mobile-friendly. scripts.js: Handles the login and logout functionality. It uses localStorage to persist the logged-in state.

OpenAI o1 coding ability reviews of reasoning with LLM. In their official website, the prompt for OpenAI o1 is to "takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format."After comparing the results of o1 with GPT4o and the final scripts are actually much longer, o1 results have 70 lines of scripts but the GPT4o has only 31 lines of code. The key difference is how to "Build output string". Source: https://openai.com/index/learning-to-reason-with-llms/

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

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

ChatGPT did a good job on this python code test, which is to "implement self attention layer in transformer using pytorch package". Overall, the python code is correct and concise. After comparing ChatGPT vs Gemini vs Claude for coding, I find that the overall response from ChatGPT lacks of detailed explanation of meanings of each section of the code, which is less helpful than the response from Claude Sonnet.

My question for ChatGPT is "Show me the latex code of KL Divergence". Even though it gives me the latex code for KL Divergence already in the front, it provides a lot of unrelated information of basic latex usage such as how to include the package, which is not very relevant to my original intent.

I asked ChatGPT to write the python code for QuickSort Implementation. The results are attached. Pretty Amazing, right?

ChatGPT provides very good and clear answers to my question "Please give me some introduction of Maxwell equations". The best part is that it also display the equation in beautiful format with latex code generated, such as the equation in Gauss's Law for Electricity and others. Compared to the answers by Gemini, it definitely wins.