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license: llama2 |
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# LongLLaMA-Code 7B Instruct |
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<th style="font-size: 120%"> >_ 🎓 <a href="https://huggingface.co./syzymon/long_llama_code_7b_instruct">LongLLaMA-Code 7B Instruct</a> 📑🗨 </th> |
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<a href="https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_code_instruct_colab.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg"></a> |
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## TLDR |
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[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). |
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## Tuning |
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### Code |
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The model was tuned on a TPU v3-128 pod with 128 batch size. |
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For tuning, we have used the data preparation pipeline available in instruction_fine_tuning. |
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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). |
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### Training |
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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). |
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All inputs were truncated and randomly padded (left/right) to 3072 tokens. |
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The last context length was set to 1536. |
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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. |
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The optimizer was adamw. |
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The question prompt (`pre_question_text`) was: |
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``` |
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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 |
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``` |
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To trigger the model answer one can use: |
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``` |
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\nAnswer: |
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``` |
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The chat prompt was: |
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``` |
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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 |
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``` |
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To denote the assistant one can write: |
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``` |
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\nASSISTANT: |
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``` |
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To denote the user one can write: |
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``` |
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\nUSER: |
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``` |
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### Datasets and sampling probability |
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* 0.71 - [TIGER-Lab/MathInstruct](https://huggingface.co./datasets/TIGER-Lab/MathInstruct) |
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* 0.16, - [Open-Orca/OpenOrca](https://huggingface.co./datasets/Open-Orca/OpenOrca) questions with less than 5k chars |
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* 0.08, - [Open-Orca/OpenOrca](https://huggingface.co./datasets/Open-Orca/OpenOrca) questions above 5k chars but below 12k chars |
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* 0.02 - [zetavg/ShareGPT-Processed](https://huggingface.co./datasets/zetavg/ShareGPT-Processed) conversations below 6k chars |
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* 0.01 - [zetavg/ShareGPT-Processed](https://huggingface.co./datasets/zetavg/ShareGPT-Processed) conversations above 6k chars but below 12k chars |
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To improve the quality of the data, the datasets were filtered using regular expressions. |
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## License |
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The instruction/chat-tuned models are for research purposes only. |
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[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. |
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## Acknowledgements |
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We gratefully acknowledge the TPU Research Cloud program, which was instrumental to our research by providing significant computational resources. |
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