https://higgsfield.ai
We have a massive GPU cluster and developed our own infrastructure to manage the cluster and train massive models.

There’s how it works:

  1. You upload the dataset with preconfigured format into HuggingFaсe [1].
  2. Choose your LLM (e.g. LLaMa 70B, Mistral 7B)
  3. Place your submission into the queue
  4. Wait for it to get trained.
  5. Then you get your trained model there on HuggingFace.

Essentially, why would we want to do it?

  1. We already have an experience with training big LLMs.
  2. We could achieve near-perfect infrastructure performance for training.
  3. Sometimes GPUs have just nothing to train.

Thus we thought it would be cool if we could utilize our GPU cluster 100%. And give back to Open Source community (already built an e2e distributed training framework [2]).

This is in an early stage, so you can expect some bugs.

Any thoughts, opinions, or ideas are quite welcome!

[1]: https://github.com/higgsfield-ai/higgsfield/blob/main/tutori…

[2]: https://github.com/higgsfield-ai/higgsfield

      • light24bulbs@alien.topBEnglish
        1·
        10 months ago

        Are you having good luck with adding knowledge to the model? I tried this with llama for a couple weeks when things were just getting going and I just could not find good hyperparameters for fine tuning. I was also doing Lora so…idk.

        • higgsfield_ai@alien.topOPBEnglish
          1·
          10 months ago

          From our experience, to get a very good results you need

          1. High quality dataset. It’s worth to spend more time on data cleaning. It’s way better to have a smaller dataset with high quality points than a huge dataset with garbage.

          2. You need to fully finetune it.