--- license: llama2 --- # LongLLaMA-Code 7B Instruct
>_ 🎓 LongLLaMA-Code 7B Instruct 📑🗨
## TLDR [LongLLaMA-Code 7B Instruct](https://huggingface.co/syzymon/long_llama_code_7b_instruct) is [LongLLaMA-Code 7B](https://huggingface.co/syzymon/long_llama_code_7b) tuned on [TIGER-Lab/MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca), and [ShareGPT-Processed](https://huggingface.co/datasets/zetavg/ShareGPT-Processed) datasets. It can answer basic questions about research papers and code. It can also perform a simple code refactoring. You can try the quantized version of the model using a free GPU in [Google Colab](https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_code_instruct_colab.ipynb). ## Tuning ### Code The model was tuned on a TPU v3-128 pod with 128 batch size. For tuning, we have used the data preparation pipeline available in instruction_fine_tuning. However, we have replaced the Hugging Face Trainer with a modification of FoT continued pretraining code. This modification boils down to propagating the memory cache throughout the model (basically reproducing the Pytorch inference code functionality in JAX). ### Training Here, we present the basic information about how the model was tuned. For more details, see the [GitHub repo](https://github.com/CStanKonrad/long_llama/tree/main/instruction_fine_tuning/misc). All inputs were truncated and randomly padded (left/right) to 3072 tokens. The last context length was set to 1536. The model was trained for 9k steps, started with a learning rate of 1.2e-5, 700 steps of warmup, and finished with a learning rate of 0. The optimizer was adamw. The question prompt (`pre_question_text`) was: ``` You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can.\n\n ``` To trigger the model answer one can use: ``` \nAnswer: ``` The chat prompt was: ``` A chat between a user (denoted as USER:) and an artificial intelligence assistant (denoted as ASSISTANT:). The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n ``` To denote the assistant one can write: ``` \nASSISTANT: ``` To denote the user one can write: ``` \nUSER: ``` ### Datasets and sampling probability * 0.71 - [TIGER-Lab/MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) * 0.16, - [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) questions with less than 5k chars * 0.08, - [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) questions above 5k chars but below 12k chars * 0.02 - [zetavg/ShareGPT-Processed](https://huggingface.co/datasets/zetavg/ShareGPT-Processed) conversations below 6k chars * 0.01 - [zetavg/ShareGPT-Processed](https://huggingface.co/datasets/zetavg/ShareGPT-Processed) conversations above 6k chars but below 12k chars To improve the quality of the data, the datasets were filtered using regular expressions. ## License The instruction/chat-tuned models are for research purposes only. [LongLLaMA-Code 7B Instruct](https://huggingface.co/syzymon/long_llama_code_7b_instruct) is [LongLLaMA-Code 7B](https://huggingface.co/syzymon/long_llama_code_7b) tuned on [TIGER-Lab/MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca), and [ShareGPT-Processed](https://huggingface.co/datasets/zetavg/ShareGPT-Processed) datasets. Note that those datasets contain outputs from ChatGPT. See also the [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) license. ## Acknowledgements We gratefully acknowledge the TPU Research Cloud program, which was instrumental to our research by providing significant computational resources.