对于Python 微调,论文将初始学习率设置为 1e−4。对于 Code Llama - Instruct,他们用一个批次大小为 524,288 个 token 进行训练,总共训练了大约 50 亿个 token。 Long Context finetuning 对于长上下文微调 (LCFT),作者使用了2e−5的学习率 16384的序列长度,并重置 RoPE 频率,基值为θ = 10^6 ...
长上下文微调 (Long context fine-tuning) 基于transformer的语言模型中的一个核心研究点是如何有效处理长序列(参考Vaswani等人,2017年的工作)。这涉及到两大挑战:首先是外推,意指模型如何处理那些超出原始训练数据长度的序列;其次是注意力机制的二次复杂度问题,导致模型在训练时更倾向于短或中等长度的输入。 Code Ll...
通过长序列微调(long context fine-tuning),CodeLLaMA系列模型支持高达10万个tokens的输入文本,明显优于只支持4K的Llama 2。在非常长的代码文件中仍表现稳定。 在Python代码生成基准测试数据集如HumanEval和MBPP上取得最先进的成绩,尤其是与开源模型相比,基本是最强的。同时也在多语言数据集MultiPL-E上表现强劲。 Code...
由于CodeLlama-70B-Instruct是开源的预训练模型,相比较榜单其它模型,其优势非常明显。其他模型大多数是微调或者闭源模型。 根据官网的论文介绍,CodeLLaMA的特点如下: 通过长序列微调(long context fine-tuning),CodeLLaMA系列模型支持高达10万个tokens的输入文本,明显优于只支持4K的Llama 2。在非常长的代码文件中仍表现...
(PEFT) for efficient fine-tuning of large models. With this method, you freeze the whole model and only add a small set of adjustable parameters or layers into the model. For instance, instead of training all 7 billion parameters for Llama 2 7B, you can fine-tune less than 1% of the ...
This repository is adapted from https://github.com/pacman100/LLM-Workshop, which supports fine-tuning a number of models, including Code Llama. However, a number of problems were encountered when using the original repository with Code Llama. This repository contains improvements like context-level...
There is no need to make supervised fine-tuning upon the fine-tuned context extended models. It is all right to directly use base model as Llama2-chat models, as the amount of long instruction following data is enough for SFT. Our long instruction following data can be found inLongAlpaca-...
Code Llama – Instruct is an instruction fine-tuned and aligned variation of Code Llama. Instruction tuning continues the training process, but with a different objective. The model is fed a natural language instruction input and the expected output. This makes it better at understanding what peopl...
Paper Code Llama 2: Open Foundation and Fine-Tuned Chat Models facebookresearch/llama • • 18 Jul 2023 In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.19 ...
In this article, we will explore how users can use Code Llama on Azure to unleash the full potential of AI-driven coding for their tasks.