None of the 3B and 7B models are at ChatGPT’s level, let alone GPT-4. The 13B models start doing really interesting things, but you don’t get near ChatGPT results until you move up to the best 30B and 65B models, which require beefier hardware. Nothing out there right now approximates GPT-4.
The big story here for me is that the difference in training set is what makes the difference in quality. There is no secret sauce, the open source architectures do well, provided you give them a large and diverse enough training set. That would mean it is just a matter of pooling resources to train really capable open source models. That makes what RedPajama is doing, compiling the best open dataset, very important for the future of high quality open source LLM’s.
If you want to play around with this yourself you can install oobabooga and figure out what model fits your hardware from the locallama reddit wiki. The llama.cpp 7B and 13B models can be run on CPU if you have enough RAM. I’ve had lots of fun talking to 7B and 13B alpaca and vicuna models running locally.
LLaVA 13B is a great multimodal model that has first class support in oobabooga too.
It's really fun to enable both the whisper extension and the TTS extension and have two-way voice chats with your computer while being able to send it pictures as well. Truly mind bending.
Quantized 30B models run at acceptable speeds on decent hardware and are pretty capable. It's my understanding that the open source community is iterating extremely fast on small model sizes getting the most out of them by pushing the data quality higher and higher, and then they plan to scale up to at least 30B parameter models.
I really can't wait to see the results of that process. In the end you're going to have a 30B model that's totally uncensored and is a mix of Wizard + Vicuna. It's going to be a veryyyy capable model.
I usually even prefer GPT-3.5, as it's faster and much cheaper. GPT-4 is great for the hardcore logical reasoning, but when I want something that knows to turn my lights on and turn the TV to a channel, it's overkill.
It's not even that bad. Core i7-12700K with DDR5 gives me ~1 word per second on llama-30b - that is fast enough for real-time chat, with some patience. And things are even better on M1/M2 Macs.
The critical factor seems to be the ability to fit the whole model in RAM (--mlock option in oobabooga). With Apple's RAM prices most M1/M2 owners probably don't have the 32 GB RAM required to fit a 4bit 30B model.
The big story here for me is that the difference in training set is what makes the difference in quality. There is no secret sauce, the open source architectures do well, provided you give them a large and diverse enough training set. That would mean it is just a matter of pooling resources to train really capable open source models. That makes what RedPajama is doing, compiling the best open dataset, very important for the future of high quality open source LLM’s.
If you want to play around with this yourself you can install oobabooga and figure out what model fits your hardware from the locallama reddit wiki. The llama.cpp 7B and 13B models can be run on CPU if you have enough RAM. I’ve had lots of fun talking to 7B and 13B alpaca and vicuna models running locally.
https://www.reddit.com/r/LocalLLaMA/wiki/models/