在Llama-3的报告中,任何在pre-training之后发生的训练都属于post-training,包括SFT、DPO等。 Llama-3的post-training不是一次完成的,而是多个round迭代进行,整个post-training包含6轮的SFT和DPO。 1.Modeling post-training的流程如下图 1.1.Chat Dialog Format Llama-3相比之前的版本多了一些能力,比如tool use。在...
另一方面,同Llama-2一样,preference data中只有区分度比较大的数据对用于训练RM。 数据上,除了常规的chosen和rejected response之外,还引入了第三种 -- “edited response”,即在chosen的基础上通过(人工)编辑,进一步提升这条response的质量。这样每条ranking sample就可能有3条response(edited > chosen > rejected)。
这些观察结果与Gemini Team、Llama2和LIMA的报告结果一致。作者使用以下技术来提升prompt distribution selection, response formatting和COT data formatting: prompt distribution selection:从WizardLM中获得灵感,开发复合指令并逐步进化它们以增加其复杂性。这种方法显著减少了实验所需的SFT数据大小; response formatting:...
Llama3.1的post-training流程包括多个关键步骤,其中Modeling是核心之一。在Modeling阶段,Meta AI设计了一系列策略来优化模型的对话格式(Chat Dialog Format)和奖励模型(Reward Modeling)。 Chat Dialog Format:Llama3.1支持多消息聊天协议,能够处理复杂的对话场景。例如,在工具使用(Tool Use)场景下,模型可能需要生成多个结果...
An example command for converting a GPT model from the old format (legacy) to the new format (core) would look as follows: For examples of converting Llama/Mistral models into Megatron, please seehere. Megatron offers multiple checkpoint formats, including: ...
Llama-2-70B (8-bit) fine-tuning using LoRA on a single GPU Preparing Training Data To reproduce fine-tuned model on doping task, first adjust the training data path indatasets.pyandcustom_dataset.pyto point to training data and test data inNERRE doping repo,NERRE general and MOF repo. ...
LLaMA: open and efficient foundation language models. arXivorg. 2023;2302.13971. https://doi.org/10.48550/arxiv.2302.13971. Radianti J, Majchrzak TA, Fromm J, Wohlgenannt I. A systematic review of immersive virtual reality applications for higher education: design elements, lessons learned, and ...
LoRAstands for Low-Rank Adaptation. These models allow for the use of smaller appended models to fine-tune diffusion models. In short, the LoRA training model makes it easier to train Stable Diffusion (as well as many other models such as LLaMA and other GPT models) on different concepts, ...
本文主要针对HuggingFace开源的 transformers,以BERT为例介绍其源码并进行一些实践。主要以pytorch为例 (tf 2.0 代码风格几乎和pytorch一致),介绍BERT使用的Transformer Encoder,Pre-training Tasks和Fine-tuning Tasks。最后,针对预训练好的BERT进行简单的实践,例如产出语句embeddings,预测目标词以及进行抽取式问答。本文主要面...
LlamaIndex is a powerful indexing tool designed to enhance the capabilities of Large Language Models (LLMs) by allowing them to retrieve and utilize custom data sets effectively. This instructor-led, live training (online or onsite) is aimed at intermedi