Are you looking to build your own ChatGPT-style service? If so, you’ll be excited to hear about the new open-source implementation of LLaMA based on Reinforcement Learning from Human Feedback (RLHF): ChatLLaMA.
LLaMA is an exciting new collection of large language models ranging from 7 to 65 billion parameters. It is smaller than GPT-3 but has better performance, and it opens the door to faster inference performance and chatGPT-like real-time assistants while being cost-effective and running on a single GPU.
Today, Nebuly has introduced ChatLLaMA, the first open-source implementation of LLaMA based on RLHF. ChatLLaMA allows you to easily train LLaMA-based architectures in a similar way to ChatGPT using RLHF. It has built-in support for DeepSpeed ZERO to speed up the fine-tuning process, and it supports all LLaMA model architectures (7B, 13B, 33B, 65B).
In this blog post, we’ll discuss the features of ChatLLaMA, how to use it, and how you can join Nebuly’s efforts toward more efficient and open ChatGPT-like assistants. Let’s get started!
Features of ChatLLaMA
ChatLLaMA has a complete open-source implementation that enables you to build a ChatGPT-style service based on pre-trained LLaMA models. Compared to the original ChatGPT, the training process and single-GPU inference are much faster and cheaper by taking advantage of the smaller size of LLaMA architectures.
The library also supports all LLaMA model architectures (7B, 13B, 33B, 65B), so that you can fine-tune the model according to your preferences for training time and inference performance.
How to Use ChatLLaMA
If you want to use ChatLLaMA, you should provide Meta’s original weights and your custom dataset before starting the fine-tuning process. Alternatively, you can generate your own dataset using LangChain’s agents.
Below is the code to start the training in the case of ChatLLaMA 7B:
from chatllama.rlhf.trainer import RLTrainer
from chatllama.rlhf.config import Config
path = "path_to_config_file.yaml"
config = Config(path=path)
trainer = RLTrainer(config.trainer)
trainer.distillate()
trainer.train()
trainer.training_stats.plot()
Nebuly has open-sourced the complete code to replicate the ChatLLaMA implementation, opening up the possibility for every user to fine-tune their own personalized ChatLLaMA assistants. The library can be further extended with the following additions:
– Checkpoints with fine-tuned weights
– Optimization techniques for faster inference
– Support for packaging the model into an efficient deployment framework
How to Join Nebuly’s Efforts
If you like the project, please consider leaving a star on the GitHub repository. All developers are invited to join Nebuly’s efforts toward more efficient and open ChatGPT-like assistants.
You can participate in the following ways:
1. Submit an issue or PR on GitHub
2. Join their Discord group to chat
We hope this blog post has been helpful in introducing you to the exciting new open-source implementation of LLaMA based on RLHF: ChatLLaMA. We invite you to join Nebuly’s efforts toward more efficient and open ChatGPT-like assistants.
Note: Thanks to Nebuly’s team for the thought leadership/ Educational article above.