🟢使用多个强大的模型和工具: 1️⃣Google-BERT:用于高效的文本分块 2️⃣LLaMA 3.1 70B:生成高质量的训练数据集 3️⃣LLaMA 3.1 8B:作为我们微调的目标模型 4️⃣Axolotl:一个简单易用的开源微调框架 🟢视频内容包括: 1️⃣文本分块的重要性及其在AI训练中的作用 2️⃣使用Google-BERT...
python-m llama_recipes.finetuning --use_peft --peft_method lora --quantization --model_name ../llama/models_hf/7B --output_dir ../llama/PEFT/model # multiple GPUs torchrun --nnodes 1 --nproc_per_node 1 examples/finetuning.py --enable_fsdp --use_peft --peft_method lora --model_...
fine-tuning all weights. partial-parameter freeze some weights and change some weights, set layers.trainable=True or False to let them to be trainable or not. LoRA QLoRA command parameter fp16 here are some data types used in NVIDIA GPU, such as fp16, fp32, bf16, tf16, tf32, and I...
对llama3进行全参微调、lora微调以及qlora微调。. Contribute to taishan1994/Llama3.1-Finetuning development by creating an account on GitHub.
usp=sharing) 5.Kaggle笔记本每周免费提供30小时GPU:Llama 3.2 Vision(11B)[Kaggle Notebook](https://www.kaggle.com/code/danielhanchen/llama-3-2-vision-finetuning-unsloth-kaggle)Qwen 2 VL(7B)[Kaggle笔记本](https://www.kaggle.com/code/danielhanchen/qwen2-vision-finetuning-unsloth-kaggle) 6....
menu Create Shashank Sharma+1 ·5mo ago· 34 views arrow_drop_up0 Runtime play_arrow 3s Language Python
Llama3/3.1-Finetuning 对llama3进行全参微调、lora微调以及qlora微调。除此之外,也支持对qwen1.5的模型进行微调。如果要替换为其它的模型,最主要的还是在数据的预处理那一块。 更新日志 2023/07/28:添加对Baichuan2-7B-Chat的微调。 2024/07/24:添加对llama3.1-8B-Instruct的微调。transformers==4.43.1和acceler...
In this hands-on workshop, we‘ll discuss the unique challenges in finetuning LLMs and show you how you can tackle these challenges with open-source tools through a demo. By the end of this session, attendees will understand: - How to fine-tune LLMs like Llama-2-7b on a single GPU...
Fine_Tuning_LLamaNotebookInputOutputLogsComments (0)Output Data lora-alpaca chevron_right Loading... Outputmore_vert arrow_right folder lora-alpaca Download notebook output navigate_nextminimize content_copyhelpCould not load files: Unexpected end of JSON input...
TC-Llama 2 addresses these limitations by utilizing the advanced generalization capabilities of LLMs, specifically adapting them to this intricate domain. Our model, based on the open-source LLM framework, Llama 2, is customized through instruction tuning using bilingual Korean-English datasets. Our ...