Wanna see what Nostr does to AI?
Check out the pics. The red ones are coming from default Llama 3.1. The green ones are after training with Nostr notes.
If you want to download the current version of the LLM:
https://huggingface.co/some1nostr/Nostr-Llama-3.1-8B
The trainings are continuing and current version is far from complete. After more trainings there will be more questions where it flipped its opinion..
These are not my curation, it is coming from a big portion of Nostr (I only did the web of trust filtration).
What is your method & tool for fine-tuning this model(s)?
I've been desiring to train some LLM's on specific datasets and seeking a method(s)/tool(s) to do so best fit for me
Second question; what is your dataset structure? I understand kind 1 & other events, but how is it structured when feeding the LLM? Just JSON? Anything else I'm missing to train & fine-tune my own LLM?
If you don’t mind me giving you a suggestion. An easy way to get started is by using Unsloth’s Google Colab notebooks. Just by inspecting the code of some of their many notebooks you can get a solid starting point about the fine-tunneling steps, including the dataset formats. https://unsloth.ai
Thank you I'll give this a test
I see this is for smaller models. Can I use this as well for ~100B parameter LLM's?
Would prefer to do locally if I can; I do have access to hardware to do this
Yes, you can. These notebooks use smaller models only to take advantage of the Tesla T4 (free tier). You can mod the notebook and use it locally. You can use their bigger models or any other that you want when you feel more comfortable with the different model templates. https://docs.unsloth.ai/get-started/all-our-models
Thank you for your follow up answers; much appreciated🦾
Download all the notes.
Take the "content" field from the notes and change the name to "text":
Previously:
{"id":".....................", "pubkey": ".................", "content": "gm, pv, bitcoin fixes this!", .......}
{"id":".....................", "pubkey": ".................", "content": "second note", .......}
Converted into jsonl file:
{"text": "gm, pv, bitcoin fixes this!" }
{"text": "second note" }
Used Unsloth and ms-swift to train. Unsloth needed to convert from base to instruct. This is a little advanced. If you don't want to do that and just start with instruct model, you can use ms-swift or llama-factory.
You will do lora, pretraining. I used 32 as lora rank but you can choose another number.
Excellent I figured that was structure. Thank you for the detailed information
