GPT4All: Model Training, Model Access, and Model Evaluation | HackerNoon

Table of Links

Abstract and 1. Introduction

2 The Original GPT4All Model

2.1 Data Collection and Curation

2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation

3 From a Model to an Ecosystem

3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License

3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem

3.3 The Current State of GPT4All

4 The Future of GPT4All

Limitations and References

2.2 Model Training

The original GPT4All model was a fine tuned variant of LLaMA 7B. In order to train it more efficiently, we froze the base weights of LLaMA, and only trained a small set of LoRA (Hu et al., 2021) weights during the fine tuning process. Detailed model hyper-parameters and training code can be found in our associated code repository[1].

2.3 Model Access

We publicly released all data, training code, and model weights for the community to build upon. Further, we provided a 4-bit quantized version of the model, which enabled users to run it on their own commodity hardware without transferring data to a 3rd party service.

Our research and development costs were dominated by ∼$800 in GPU spend (rented from Lambda Labs and Paperspace) and ∼$500 in OpenAI API spend. Our final GPT4All model could be trained in about eight hours on a Lambda Labs DGX A100 8x 80GB for a total cost of ∼$100.

2.4 Model Evaluation

We performed a preliminary evaluation of our model using the human evaluation data from the Self Instruct paper (Wang et al., 2023). We reported the ground truth perplexity of our model against what was, to our knowledge, the best openly available alpaca-lora model at the time, provided by user chainyo on HuggingFace. Both models had very large perplexities on a small number of tasks, so we reported perplexities clipped to a maximum of 100. We found that GPT4All produces stochastically lower ground truth perplexities than alpaca-lora (Anand et al., 2023).

[1] https://github.com/nomic-ai/gpt4all