"created_at": 1680619086, "filename": "dataset_prepared.jsonl", "id": "file-WSkHmtSfBLORMAhEoEVyBDO4", "object": "file", "purpose": "fine-tune", "status": "processed", "status_details": null }, ], "object": "list" } ...
1、下载好7B、llama-lora、alpaca-lora到model_hub下。进入到model_hub目录下。2、将llama转换为hugging...
A WebUI for LLM fast finetuning on GPU or CPU. typescript webui llama train lora finetune llm Updated Aug 12, 2024 TypeScript hustcc / miz Star 22 Code Issues Pull requests 🎯 Generate fake data for finetune of AI, Just like a person. faker mocker fake-data finetune finetune...
基于13B的LLAMA模型,70w的数据,4个GPU进行fine-tune,epoch=1~3,但是每次记录的loss特别大,最开始的lr却是0,而eval_loss却是Nan batch_size=256; micro_batch_size=8; eval_steps=200; save_steps=200; test_size = 10000;
Dataset component to load the dataset sh-4.2$ cat config_l3.1_8b_lora.yaml# Model Argumentsmodel:_component_:torchtune.models.llama3_1.lora_llama3_1_8b lora_attn_modules:['q_proj','v_proj']lora_rank:8lora_alpha:16# Tokenizertokenizer:_component_:torchtune.models.llama3...
这篇文章介绍了近年最火的预训练大模型之一LLaMA,以及如何对它进行finetune,以应用到下游NLP、多模态等任务中,也包括如何降低finetune的资源开销,实现高性价比的大模型应用。 1、LLaMA大模型 LLaMA是今年2月份由MetaAI提出的一组预训练大模型,相关论文为LLaMA: Open and Efficient Foundation Language Models。LLaMA完...
Figure 1. Llama 2 7B Fine-Tuning Performance on Intel® Data Center GPU Refer to Configurations and Disclaimers for configurations In a single-server configuration with a single GPU card, the time taken to fine-tune Llama 2 7B ranges from 5.35 hours with one Intel® Data Cent...
Llama3是Meta开发的最新一代大型语言模型(LLM)。这些模型是在15万亿token的广泛数据集上训练的(相比之下,Llama2的训练数据集为2万亿token)。发布了两种模型尺寸:一个700亿参数的模型和一个更小的80亿参数的模型。700亿参数的模型已经展示了令人印象深刻的性能,在MMLU基准测试中得分为82,在HumanEval基准测试中得分为...
For simplicity, we show how to fine-tune and deploy the Meta Llama 3.1 405B model on a single ml.p5.48xlarge instance. Let’s load and process the dataset in conversational format. The example dataset for this demonstration is OpenAssistant’s TOP-1 ...
When constructing the final system message for the training data, we also modify the original instruction half of the time to be less verbose, e.g., “Always act as Napoleon from now”->”Figure: Napoleon.” These steps produce an SFT dataset, on which we can fine-tune Llama 2-Chat. ...