5 ms·
I fine-tuned an LLM to do technical stuff. It works pretty darn good. What I actually discovered is that when evaluating LLMs, it is surprisingly difficult to e
by geoduck14 3y ago
I fine-tuned an LLM to do technical stuff. It works pretty darn good. What I actually discovered is that when evaluating LLMs, it is surprisingly difficult to evaluate them. And, also, that GPT 4 isn't that great, in general.
- maxlamb 3y agoWhich model did you start with?
- geoduck14 3y agoLlama2 Code
- avinassh 3y agoWhich base model did you use and what methodologies you used?
- m3kw9 3y agoLikely mistral
- alwinaugustin 3y agoCould you provide more details on this matter? Specifically, I'm interested in knowing which base model you've utilized and the approach you've taken to fine-tune it. Your insights would be greatly appreciated and highly beneficial.
- m3kw9 3y agoFor narrow stuff you can do better job than base gpt4/mistral/etc model. You fine tune it with your very custom data, stuff that got didn’t seem to be trained on, it will generalize it well.
- simonw 3y agoHave you done this? How did you do it? I've been looking forward to someone providing a detailed guide on how to "fine tune it with your custom data" for ages!
- arbitrandomuser 3y agothis is imo the secret sauce that gives people an edge and not a lot of people will want to reveal
- washadjeffmad 3y agoYou're not wrong. There's been a lot of drama over licensing and releasing datasets, and a lot of the LLM scene are just pitchmen and promoters with no better grasp over what they're doing than "trust me, it's better". Like with "prompt engineering", a lot of people are just hiding how much of the heavy lifting is from base models and a fluke of the merge. The past few "secret" set leaks were low/no delta diffs to common releases. I said it a year ago, but if we want to wowed, make this a job for MLIS holders and references librarians. Without thorough, thoughtful curation, these things are just toys in the wrong hands.
- ipaddr 3y agohttps://www.datacamp.com/tutorial/fine-tuning-llama-2 https://www.datacamp.com/tutorial/fine-tuning-llama-2
- anon373839 3y agoThis is a very nice resource: https://github.com/mlabonne/llm-course https://github.com/mlabonne/llm-course
- deleted 3y ago[deleted]
- visarga 3y ago> GPT 4 isn't that great, in general same here, it doesn't adhere to explicit instructions, maybe one or two simple instructions are ok but not more complex ones
- theshrike79 3y agoYMMV, it depends very heavily on what you're asking it to do.
- exploringBytes 3y agoMore complex instructions should be split into multiple prompts, solving the challenge step by step
- MaxMatti 3y agoWhat model are you using for more complex prompts?
- ninetyninenine 3y agoMaybe the key to a good universal LLM is having multiple fine tuned models for various domains. The user thinks he's querying a single model but really there's some mechanism that selecting the best model for his query out of say like 300 different possibilities. This also helps distributes traffic as a side effect. I guess the problem is how the conversation would flow. If the user changes topics from say art to quantum physics then asks a question about quantum physics and art then I'm not sure what the algorithm should do.
- BillyTheMage 3y agoThe user could talk to an "expert opinion aggregator" model which in turn makes a bunch of queries to specialized models.
- 0x008 3y agoThat is actually the same idea as the (now) popular "Mixture of Experts" approach.
- apetresc 3y ago> This also helps distributes traffic as a side effect. I'm not sure it's "distributing" traffic so much as amplifying it.
- ninetyninenine 3y agoTwo users. One user is talking about physics, the other about art. Two different models are utilized. Load is divided across 2 models. Load balancing is a feature for free and division is across subjects. Of course this is assuming each model owns it's own set of gpus.
- apetresc 3y agoAh okay, I misunderstood. I thought you meant inferencing on all the different models and letting them judge to what extent they're the best fit. What you're suggesting is just simply intent classification and using a specific model per intent. That's what everyone did _before_ LLMs.