Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization[32]. Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, etc.arXiv 2023. 3.33 SelfCheck 最近,大型语言模型(LLMs)的进展,特别是链式思维提示的发明,使逐步推理自动回答问题成为可能。然而,当面临需要非线性思维的更复杂问题时,...
finetuningTheOliveconfiguration files to execute the fine-tuning job. Olive is an easy-to-use hardware-aware model optimization tool that composes industry-leading techniques across model compression, optimization, and compilation. Given a model and targeted hardware, Olive composes the best suitable ...
Neural Magic is super excited about these new efforts in building Sparsify into the best LLM fine-tuning and optimization tool on the market over the coming months and we cannot wait to share more soon. Thanks for your continued support!
There are two primary approaches to fine-tuning foundation models: traditional fine-tuning and parameter-efficient fine-tuning. Traditional fine-tuning involves updating all the parameters of the pre-trained model for a specific downstream task. On the other hand, parameter...
Using human and automated evaluations, we find that classifier-free guidance yields higher-quality images. (2) Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing. (3) edits produced by the model match the style ...
In this article, we will createNeuralHermes-2.5, by fine-tuningOpenHermes-2.5using a RLHF-like technique: Direct Preference Optimization (DPO). For this purpose, we will introduce a preference dataset, describe how the DPO algorithm works, and apply it to our model. We’ll see that it...
Key Insights: (1) Is there an advantage to an agent being model-based during unsupervised exploration and/or fine-tuning? (2) What are the contributions of each component of a model-based agent for downstream task learning? (3) How well does the model-based agent deal with environmental sh...
Prepare your training and validation data Show 15 more Azure OpenAI Service lets you tailor our models to your personal datasets by using a process known as fine-tuning. This customization step lets you get more out of the service by providing: Higher quality results than what you can get...
deepspeed运行命令解读2-运行DeepSpeed-Chat/training/step2_reward_model_finetuning/training_scripts中的main.py 运行的命令: module: deepspeed.launcher.runner 运行的参数: --include="localhost:1" /home/.../代码/DeepSpeedExamples-master/applications/DeepSpeed-Chat/training/step2_reward_model_finetuning/main...
偏好学习最直接的方法是在高质量响应的人类示例上进行有监督微调(Supervised Fine-tuning,SFT),但最成功的一类方法还是利用人类(或 AI)的反馈进行强化学习(RLHF / RLAIF)。RLHF 方法根据人类偏好数据集拟合奖励模型,然后使用强化学习来优化语言模型策略,以生成能获得高奖励的响应,同时不过度偏离原始模型。虽然 RLHF...