higgsfield_ai

joined 10 months ago
 

https://higgsfield.ai/chat

Hey /r/MachineLearning, Higgsfield AI here

A few days ago, we built an easy-to-use platform for everyone in the community to finetune models. Many of you uploaded datasets, and they are waiting in the queue for training.

We received a lot of feedback, and many of you reached out, wanting the opportunity to try out the models.

We are happy to announce we made a chat interface for you to do that.

Let us know what you think.

Shout out to /u/WolframRavenwolf and his efforts in comparing the LLMs.

His post inspired the list of models we support now and we will extend it sooner.

  • HuggingFaceH4/zephyr-7b-beta
  • teknium/OpenHermes-2-Mistral-7B
  • jondurbin/airoboros-m-7b-3.1.2
  • ehartford/dolphin-2.1-mistral-7b
  • migtissera/SynthIA-7B-v1.3
  • mistralai/Mistral-7B-Instruct-v0.1
  • migtissera/SynthIA-7B-v2.0
  • teknium/CollectiveCognition-v1.1-Mistral-7B
  • ehartford/dolphin-2.2-yi-34b
  • NurtureAI/openchat_3.5-16k

Stay fine-tuned for future updates :)

[–] higgsfield_ai@alien.top 1 points 10 months ago

Hey there. Good catch, haven't realized a newer version is available. Will update soon!

[–] higgsfield_ai@alien.top 1 points 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.

[–] higgsfield_ai@alien.top 1 points 10 months ago (3 children)

We only do full fine-tune.

[–] higgsfield_ai@alien.top 1 points 10 months ago

We support only large models (starting from 7B).

 

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