Finetuning Llama-3 with MonsterGPT
Llama-3 currently holds the top position among open-source large language models (LLMs). On the Chatbot Arena Leaderboard, it leads the open-source category by a significant margin, with no comparable rivals. The performance gap between Llama-3 and GPT-4 is surprisingly narrow, and with its next release Llama-3 400B it is expected to equal GPT-4 .
Many companies are now opting out of using proprietary models like ChatGPT due to privacy concerns and the inability to share sensitive data. Instead, they are building fine-tuned versions of Llama 3 for their business-specific tasks.
These organizations prioritize customization and confidentiality, requiring models that support extensive fine-tuning and employ methodologies such as LoRA and QLoRA for targeted problem-solving.
Given these needs, Llama-3 emerges as the foremost LLM choice for many Corporate use cases.
However, given the current landscape and speed of progress in AI development, it's becoming increasingly complex and challenging for developers to build customized domain specific models. They are often faced with the tasks of figuring out appropriate hyperparameters, debugging complex ML code, setting up dataset pipelines, configuring the experiments with latest fine-tuning frameworks. This requires a deep MLOps skillset and thus results in delays for go to market and hits the developer productivity.
What if there is an AI agent that can build and deploy custom fine-tuned models for you upon your request? That too, without writing any code or even setting up complex GPU infra pipelines with ML environments and configuring incorrect hyperparameters resulting in poor model quality.
Introducing MonsterGPT, the "world's first finetuning and deployment agent" 🔥
Simply chat within ChatGPT and explain your task, and suggest using Llama 3 as your preferred model, and witness what feels like magic unfolding.
MonsterGPT runs on MonsterAPI - A No-code AI model finetuning and deployment platform.
To finetune & deploy LLMs, all you have to do is ask! In the familiar Chat UI, just ask MonsterGPT to deploy or finetune Llama-3 and it will do it.
You can specify your dataset and in case you don’t supply your dataset, the agent is smart enough to automatically recommend a dataset and setup the hyperparameter configuration appropriate for fine-tuning the model to your specifications. At MonsterAPI, we pride ourselves on providing a platform that excels in ease of use and rapid deployment, specifically tailored for fine-tuning and deploying open-source models efficiently.
MonsterAPI's technology stack incorporates several cutting-edge techniques and tools designed to optimize performance and efficiency:
📌 Flash Attention 2: This advanced attention mechanism enhances model performance by significantly reducing memory usage and computation time, allowing for faster and more efficient processing.
📌 LoRA/QLoRA: These techniques (Low-Rank Adaptation and Quantized Low-Rank Adaptation) are employed to fine-tune models with reduced resource consumption, making it easier to adapt large models to specific tasks without extensive computational overhead.
📌 Auto Batch Size: This feature automatically adjusts batch sizes to optimize memory utilization, ensuring that the available hardware resources are used efficiently and effectively during model training and inference.
📌 Low Cost GPU Cloud: MonsterAPI provides access to a cost-effective GPU cloud infrastructure, enabling users to leverage powerful computing resources without incurring prohibitive expenses, thus democratizing access to high-performance hardware.
📌 Dataset Validation API: This API ensures that datasets are correctly formatted and validated before use, reducing errors and improving the quality and reliability of the training data, which is crucial for successful model training.
📌 vLLM (with PagedAttention) for High Throughput LLM Serving: vLLM has been widely adopted across the industry, with 18K+ GitHub stars and 300+ contributors worldwide. vLLM enables high-throughput serving of large language models, facilitating quick and efficient deployment of these models for various applications.
By integrating these advanced technologies, MonsterAPI offers a comprehensive and powerful platform for developers and researchers working with open-source models.
To get started:
- Sign up on MonsterAPI, and then
- Open up MonsterGPT
Once you are inside this custom GPT interface, just give the instruction with a use-case you are fine-tuning the LLM for.
For my case, I just gave it below prompt
"I want to finetune a Llama 3 base model so that it can write python code for me following my instruction"
And based on your requirement for the finetuning task MonsterGPT will suggest you a suitable dataset, or if you want you can also specify a dataset of your choice.
In my case, I let MonsterGPT choose a proper dataset for me.
And once the dataset has been chosen, it will give me a plan for the finetuning job.
Then you will even have the option for more granular control over your finetuning job, like switching on the Quantization of weights for this training or changing the QLoRA params or dropout percentages etc.
Once you are ready and confirm, MonsterGPT will start the actual pipeline of training with the following message.
You will also get email-based notifications for any “status” changes (like queued, pending, live etc) during your finetuning job.
What's more, after the finetuning is done, you can launch the deployment of the finetuned model from ChatGPT as well. And of course, tracking of the deployment and/or termination of a running job can all be done from within ChatGPT.
Once deployed, you can query your custom fine-tuned LLM either through the ChatGPT interface by sending a prompt query or use the deployed LLM’s API endpoint, provided by MonsterAPI.
Here’s a quick walkthrough of how to deploy an LLM using MonsterGPT.
And still, if you think that you want even more granular control over your finetuning runs, you may check out their finetuning API where you can adjust the complete configuration for your experiments.
Here’s a code example of LLM Finetuning with MonsterTuner
Once setup is complete, deploying your model is as easy as clicking a button in your MonsterAPI dashboard. This enables you to start utilizing your finetuned model immediately.
So to conclude, I think it's a very novel and innovative approach and indeed a true power to be able to launch a finetuning job on your own custom dataset within minutes right from ChatGPT, by just regular chatting.
To get started, register at https://monsterapi.ai/signup and you will get 2500 Free credit which is enough for all your experimentations.
That's a wrap and here are all the important links.
👉 Website: http://monsterapi.ai
👉 MonsterGPT official guideline: https://monsterapi.ai/gpt
👉 Discord (Monsterapis) : https://discord.com/invite/mVXfag4kZN
👉 Checkout their API Docs: - https://developer.monsterapi.ai/docs/monster-deploy-beta
👉 Access all Finetuned Models by MonsterAPI here: https://huggingface.co/monsterapi