Hey LocalLLaMA. It’s Higgsfield AI, and we train huge foundational models.
We have a massive GPU cluster and developed our own infrastructure to manage the cluster and train massive models. We constantly lurked in this subreddit and learned a lot from this passionate community. Right now, we have spare GPUs, and we are excited to give back to this incredible community.
We built a simple web app where you can upload your datasets to finetune it. https://higgsfield.ai/
There’s how it works:
- You upload the dataset with preconfigured format into HuggingFaсe [1].
- Choose your LLM (e.g. LLaMa 70B, Mistral 7B)
- Place your submission into the queue
- Wait for it to get trained.
- Then you get your trained model there on HuggingFace.
[1]: https://github.com/higgsfield-ai/higgsfield/tree/main/tutorials
Registered. I am very interested and grateful to use it, but I haven’t uploaded the dataset to huggingface, so I can’t use it yet.
And I don’t understand the new learning paradigm that is done just by registering the model and dataset.
What is it that is running behind the scenes?
A very simple snippet OR code would be helpful to understand.
For example
If I give you a model and a dataset, the code will run something like this, and under what conditions will the training be finished.