python3 trl_finetune.py -m NousResearch/Llama-2-7b-hf --block_size 1024 --eval_steps 2 --save_steps 20 --log_steps 2 -tf mixtral/train.csv -vf mixtral/val.csv -b 2 -lr 1e-4 --lora_alpha 16 --lora_r 64 -e 1 --gradient_accumulation_steps 2 --pad_token_id=18610 --al...
好久没做 weekend project 了,那么,让我们来 fine-tune 自己的 LLaMA-2 吧!按照下面的步骤,我们甚至不需要写一行代码,就可以完成 fine-tunning! 第一步:准备训练脚本 很多人不知道的是,LLaMA-2 开源后,Meta 同步开源了llama-recipes这个项目,帮助对 fine-tune LLaMA-2 感兴趣的小伙伴更好地 “烹饪” 这个模型。
clone llama-recipes repository tied with llama2-tutorial, here is the directory structure, no matter where you put your data, but needs to be specified in your dataset.py code fine tuning run the following code under llama2-tutorial folder. python -m llama_recipes.finetuning \--use_peft \...
#!/usr/bin/env python # coding=utf-8 import logging import math import os import sys import random from dataclasses import dataclass, field from itertools import chain import deepspeed from typing import Dict, Optional, List, Union import datasets ...
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 Center GPU Max 1100 to 2.4 hours with one Intel® Data Center GPU Max 1550. When the configuration is scaled up to 8 GPUs, the...
The memory capacity required to fine-tune the Llama 2 7B model was reduced from 84GB to a level that easily fits on the 1*A100 40 GB card by using the LoRA technique. Resources Llama 2: Inferencing on a Single GPU LoRA: Low-Rank Adaptation of Large Language Models ...
为此,OpenCSG工程师们新益求新,与日前再次重磅开源大模型微调技术LLM-Finetune项目,构建大模型从预训练、微调、推理以及应用端到端的整体生态链。 OpenCSG开源LLM-Finetune项目的亮点 LLM-Finetune项目是一个专注于大模型微调技术的Python项目,它极大地简化了微调过程,提高了效率和可扩展性。用户可以通过以下几个步骤...
Lora Fine-tune 代码语言:javascript 代码运行次数:0 运行 AI代码解释 !accelerate launch-m axolotl.cli.train examples/openllama-3b/lora.yml 代码语言:javascript 代码运行次数:0 运行 AI代码解释 The following values were not passed to`accelerate launch`and had defaults used instead:`--num_processes`was...
Model Fine-Tuning: Configures and fine-tunes the LLaMA-2-7b model with QLoRA. Model Inference: Tests the model with sample queries and responses. Model Evaluation: Calculates metrics such as loss and perplexity. Gradio Interface: Sets up and launches the interactive chatbot. ...
Everyone is GPU-poor these days So my mission is to fine-tune a LLaMA-2 model with only one GPU and run on my laptop