Stability AI releases StableVicuna, a RLHF LLM Chatbot(stability.ai)
stability.ai
Stability AI releases StableVicuna, a RLHF LLM Chatbot
https://stability.ai/blog/stablevicuna-open-source-rlhf-chatbot
21 comments
For the tinkerers: here are some huggingface available models that are really open source (Apache or BSD license as far as I evaluated it):
- RWKV (raven 14B)
- GPT-NeoX-Chat-Base (20B)
- Flan-T5-xxl
- Fairseq Dense (13B)
- Pythia Chat Base (7B)
- Codegen (16B)
- Bloomz (7B)
Some are general, some are more specific. But I could get something out of all of them.
- RWKV (raven 14B)
- GPT-NeoX-Chat-Base (20B)
- Flan-T5-xxl
- Fairseq Dense (13B)
- Pythia Chat Base (7B)
- Codegen (16B)
- Bloomz (7B)
Some are general, some are more specific. But I could get something out of all of them.
- why fine tune vicuna and not your own model that was released last week?
- WizardLM was released a few days ago and showed promising performance for 7b, are you going to fine tune it next?
A startup with $100M in funding shouldn't focus on these optimizations.
- WizardLM was released a few days ago and showed promising performance for 7b, are you going to fine tune it next?
A startup with $100M in funding shouldn't focus on these optimizations.
Why would they build this on top of Llama when we know Meta has been going after projects using Llama?
The ambiguous licensing poisons all usage up the stack...
The ambiguous licensing poisons all usage up the stack...
This project was probably extremely affordable for them and there is value in these tests. They can play around with these different fine-tuning techniques and put them out there for people to mess with while gaining insight into better methodologies for when they spend the big time and cash on larger models.
I'm team lead at Carper. This is correct. It's also just a project one of our engineers did over the course of a few days, so very low risk.
We will swap the base model out for StableLM as soon as we can and iterate from there. We just thought the community would enjoy this research artifact :)
We will swap the base model out for StableLM as soon as we can and iterate from there. We just thought the community would enjoy this research artifact :)
Hopefully RedPajama will change things when (if) they release models based on their independent LLaMa recreation. Until that happens, it might make sense to use the LLaMa model as a stopgap for internal development.
Exactly! It seems pretty clear that someone will reproduce llama and alpaca with a commercially viable model within the next few months to a year at the longest. So if you want to build something based on top of this technology, it makes sense to build it on top of llama/alpaca internally, and then swap it out with something commercially licensed when it’s available. Then you’ve got a huge head start on anyone who is waiting for the stars to align before they start trying to build something at all.
Is there anyone compiling an in-depth comparison between different models that are emerging? Are there standardized benchmarks?
Nope and I'd really like to see a yearly benchmark to create some sort of feature rating!
I feel like I see charts floating around r/LocalLLaMA/ from time to time. Might be some more info there.
I feel like there's something I'm missing about Hugging Face. Every time I try any demo of any model on the website I can never even get a response before it times out.
Why is it trained on GPT generated content? Won't it reduced the content quality instead of improving it? GPT generates lots of fluff which will only pollute the new models further.
The vicuna model has shown that there is value in this method.
https://vicuna.lmsys.org/
https://vicuna.lmsys.org/
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yet another model using the stanford alpaca dataset, what a waste of time.
In terms of getting work done 30B and under llamas aren't going to be very helpful. Maybe 65B behaves differently but the 30B and under are just amazing toys; not tools. And, again, if you fine-tune them they get significantly stupider. Think of fine tuning as a lobotomy. The model is more likely to stay on the rails but it loses emergent capabilities.