FlagAI支持最高百亿参数的悟道GLM(详见GLM介绍),同时也支持BERT、RoBERTa、GPT2、T5 模型、Meta OPT模型和 Huggingface Transformers 的模型。 FlagAI提供 API 以快速下载并在给定(中/英文)文本上使用这些预训练模型,你可以在自己的数据集上对其进行微调(fine-tuning)或者应用提示学习(prompt-tuning)。 FlagAI提供丰...
标题:CMU | FINETUNA: Fine-tuning Accelerated Molecular Simulations(FINETUNA: 微调加速分子模拟) 作者:Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, Zachary Ulissi 简介:本文研究预训练与微调在分子模拟上的应用。机器学习方法有可能以一种计算效率高的方式来近似密度泛函理论(DFT),这可以极大地提高计算模拟对...
In addition, few-shot learning was explored by fine-tuning Open Pre-Trained Transformers (OPT; a large language model [LLM] with publicly available weights) using a small annotated subset and zero-shot learning using ChatGPT (an LLM without available weights). Results: The comparison of ...
今年五月,MetaAI官宣发布了基于1750亿参数的超大模型OPT-175B,还对所有社区免费开放。 12月22日,该模型的更新版本OPT-IML(Open Pre-trained Transformer)正式上线,Meta称其「对2000个语言任务进行了微调,包含1750 亿个参数」,还将为非商业研究用途免费开放。 这次更新的OPT-IML的性能表现如何,先上两张图来看看。
步骤1:监督微调(Supervised Fine-Tuning,SFT)阶段,收集示例数据并训练一个监督学习模型。从提示数据集中抽取一个提示内容,由标注人员编写答案,最后使用监督学习方法微调GPT-3模型。 步骤2:奖励模型(Reward Modeling,RM)阶段,收集比较数据并训练一个奖励模型。对于一个提示内容,使用模型预测多个结果,然后由标注人员对答案...
Enterprises are looking for increasingly powerful compute to support their AI workloads and accelerate data processing. The efficiency gained can translate to better returns for their investments in AI training and fine-tuning, and improved user experiences for AI inference. At the Oracle CloudWorld co...
Code Issues Pull requests Train very large language models in Jax. deep-learning flax language-models opt jax gpt2 huggingface gpt3 Updated Oct 21, 2023 Python ssbuild / llm_finetuning Star 97 Code Issues Pull requests Large language Model fintuning bloom , opt , gpt, gpt2 ,llama,ll...
Step 7: Fine Tune the Model Finally first instantiate the Trial object, fine-tuning the model, and then print the best loss with its hyperparameters values. # Initialize trials objecttrials = Trials() best = fmin( fn=hyperparameter_tuning, ...
[2024] Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark, arXiv [Paper] [2024] LLM as a Complementary Optimizer to Gradient Descent: A Case Study in Prompt Tuning, arXiv [Paper] [2024] Does Prompt Formatting Have Any Impact on LLM Performance, arXiv [...
组件种类过多,⽽且组件定义版本化变更的,未来可能需要演进成RAG⽅案⽣成⾮法json的问题,除了Fine-tuning,也可以考虑FunctionCalling的⽅案(即让模型输出flattern的简略结构,再调⽤function程序转换)问题:没有现成的⾃然语⾔到公式数据集,纯⼈⼯标注成本太⾼解决⽅案:程序+GPT数据增⼴微调数据集...