filmov
tv
Efficient Fine-Tuning for Llama-v2-7b on a Single GPU
Показать описание
The first problem you’re likely to encounter when fine-tuning an LLM is the “host out of memory” error. It’s more difficult for fine-tuning the 7B parameter Llama-2 model which requires more memory. In this talk, we are having Piero Molino and Travis Addair from the open-source Ludwig project to show you how to tackle this problem.
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
- Techniques like parameter efficient tuning and quantization, and how they can help
- How to train a 7b param model on a single T4 GPU (QLoRA)
- How to deploy tuned models like Llama-2 to production
- Continued training with RLHF
- How to use RAG to do question answering with trained LLMs
This session will equip ML engineers to unlock the capabilities of LLMs like Llama-2 on for their own projects.
This event is inspired by DeepLearning.AI’s GenAI short courses, created in collaboration with AI companies across the globe. Our courses help you learn new skills, tools, and concepts efficiently within 1 hour.
Here is the link to the notebook used in the workshop:
Speakers:
Piero Molino, Co-founder and CEO of Predibase
Travis Addair, Co-founder and CTO of Predibase
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
- Techniques like parameter efficient tuning and quantization, and how they can help
- How to train a 7b param model on a single T4 GPU (QLoRA)
- How to deploy tuned models like Llama-2 to production
- Continued training with RLHF
- How to use RAG to do question answering with trained LLMs
This session will equip ML engineers to unlock the capabilities of LLMs like Llama-2 on for their own projects.
This event is inspired by DeepLearning.AI’s GenAI short courses, created in collaboration with AI companies across the globe. Our courses help you learn new skills, tools, and concepts efficiently within 1 hour.
Here is the link to the notebook used in the workshop:
Speakers:
Piero Molino, Co-founder and CEO of Predibase
Travis Addair, Co-founder and CTO of Predibase
0:59:53
Efficient Fine-Tuning for Llama-v2-7b on a Single GPU
0:09:44
Fine Tune LLaMA 2 In FIVE MINUTES! - 'Perform 10x Better For My Use Case'
0:17:26
The EASIEST way to finetune LLAMA-v2 on local machine!
0:14:16
LLAMA-2 🦙: EASIET WAY To FINE-TUNE ON YOUR DATA 🙌
0:26:45
Steps By Step Tutorial To Fine Tune LLAMA 2 With Custom Dataset Using LoRA And QLoRA Techniques
0:18:28
Fine-tuning Llama 2 on Your Own Dataset | Train an LLM for Your Use Case with QLoRA on a Single GPU
0:28:18
Fine-tuning Large Language Models (LLMs) | w/ Example Code
0:56:16
Efficient Fine-Tuning for Llama 2 on Custom Dataset with QLoRA on a Single GPU in Google Colab
0:23:39
Beyond KV Caching: Shared Attention for Efficient LLMs
0:14:36
Fine-tune LLama2 w/ PEFT, LoRA, 4bit, TRL, SFT code #llama2
0:15:35
Fine-tuning LLMs with PEFT and LoRA
0:17:11
How to Create Custom Datasets To Train Llama-2
0:15:17
LLAMA-3 🦙: EASIET WAY To FINE-TUNE ON YOUR DATA 🙌
0:20:05
Finetuning Open-Source LLMs
0:12:59
LLAMA-2 Open-Source LLM: Custom Fine-tuning Made Easy on a Single-GPU Colab Instance | PEFT | LORA
0:27:18
fine tuning llama-2 to code
1:17:17
Fine-Tune Llama2 | Step by Step Guide to Customizing Your Own LLM
1:21:01
LLM Fine Tuning Crash Course: 1 Hour End-to-End Guide
0:00:55
Fine-tune LLama 2 in 2 Minutes on your Data - Code Example
0:12:54
🐐Llama 2 Fine-Tune with QLoRA [Free Colab 👇🏽]
0:17:06
Fine tuning Llama2 using AutoTrain HuggingFace || No code tool for fine-tuning models
0:14:45
Fine-Tune Large LLMs with QLoRA (Free Colab Tutorial)
0:24:20
'okay, but I want Llama 3 for my specific use case' - Here's how
0:28:57
Lessons From Fine-Tuning Llama-2
Комментарии