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Submitted 2 years ago by its_me_xiphos@beehaw.org to technology@beehaw.org
Comments
halcyon@slrpnk.net 2 years ago
bownage@beehaw.org 2 years ago
Good recommendations! I’d suggest doing some spacy tutorials as well, regarding the topics in the first paragraph. But arguably it’s possible nowadays to just start at transformers without any NLP knowledge, e.g. using huggingface’s AutoTrain or something similar. I wouldn’t recommend it, but you definitely could.
makingStuffForFun@lemmy.ml 2 years ago
I’m also interested, so I hope you don’t mind me joining the ride. Personally, I’d like a self hosted tool, but am happy to see what the community says.
trevron@beehaw.org 2 years ago
[deleted]TehPers@beehaw.org 2 years ago
I managed to get ollama running through Docker easily. It’s by far the least painful of the options I tried, and I just make requests to the API it exposes. You can also give it GPU resources through Docker if you want to, and there’s a CLI tool for a quick chat interface if you want to play with that. I can get LLAMA 3 (8B) running on my 3070 without issues.
Training a LLM is very difficult and expensive. I don’t think it’s a good place for anyone to start. Many of the popular models (LLAMA, GPT, etc) are astronomically expensive to train and require and ungodly number of resources.
its_me_xiphos@beehaw.org 2 years ago
Month later update: This is the route I’ve gone down. I’ve used WSL to get Ollama and WebopenUI to work and started playing around with document analysis using Llama 3. I’m going to try a few other models and see what the same document outputs now. Prompting the model to chat with the documents is…a learning experience, but I’m at the point where I can get it to spit out quotes and provide evidence for it’s interpretation, at least in Llama3. Super fascinating stuff.
Midnitte@beehaw.org 2 years ago
Using LM Studio would be even easier to get started
its_me_xiphos@beehaw.org 2 years ago
I really appreciate all the responses, but I’m overwhelmed by the amount of information and possible starting points. Could I ask you to explain or reference learning content that talks to me like I’m a curious five year old?
ELI 5?
Zworf@beehaw.org 2 years ago
Training your own will be very difficult. You will need to gather so much data to get a model that has basic language understanding.
What I would do (and am doing) is just taking something like llama3 or mistral and adding your own content using RAG techniques.
BaroqueInMind@lemmy.one 2 years ago
OLlama is so fucking slow. Even with a 16-core overclocked Intel on 64Gb RAM with an Nvidia 3080 10Gb VRAM, using a 22B parameter model, the token generation for a simple haiku takes 20 minutes.
xcjs@programming.dev 2 years ago
No offense intended, but are you sure it’s using your GPU? Twenty minutes is about how long my CPU-locked instance takes to run some 70B parameter models.
On my RTX 3060, I generally get responses in seconds.
xcjs@programming.dev 2 years ago
Ok, so using my “older” 2070 Super, I was able to get a response from a 70B parameter model in 9-12 minutes. (Llama 3 in this case.)
I’m fairly certain that you’re using your CPU or having another issue. Would you like to try and debug your configuration together?
Zworf@beehaw.org 2 years ago
Hmmm weird. I have a 4090 / Ryzen 5800X3D and 64GB and it runs really well. Admittedly it’s the 8B model because the intermediate sizes aren’t out yet and 70B simply won’t fly on a single GPU.
But it really screams. Much faster than I can read.
PS: Ollama is just llama.cpp under the hood.