LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.
As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.
Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.
view the rest of the comments
But is that a bug or a feature? I think it is plausible that relevant information is most likely either at the beginning of a document or in the previous few lines. So that is where attention should be focused.
Like when you get an assignment, the important instructions are at the beginning and not somewhere in the middle. And when writing a document or a book, the most important thing is your current sentence fits in with that paragraph. At that point you don't worry about remembering exactly what the hobbits did back in the Shire.
I remember reading some criticism on that paper. But i cannot comment on the technical aspects.
I'm my application (summarising excerpts from several papers) it is a bug. I had assumed the context would be given equal weight throughout, but the distribution of information in the generated summaries suggests it is following the lost in the middle shape. This is most evident when the early chunks of text say something contradicted by the middle. I'd expect the models to talk about the contradiction at least, but it hasn't been mentioned in any that I've looked at.
I can see what you mean, when generating text you need to pay most attention to what you just wrote, but you also don't want to claim the hobbits started out in Mordor. I have no idea how to mitigate it, other than making the context short enough that it is all 'remembered'.
If you remember where you read some criticism, I'd be very grateful for a link. That paper is doing a lot of heavy lifting in how I understand what I'm seeing, so it would be good to know where the holes in it are.
Sorry, didn't find it. If i remember correctly it was either for using models where the foundation model was trained to fewer (2048?) tokens. Or for the measurement/benchmark being too 'synthetic' / not meaningful for real-world scenarios or something.
I read this: https://www.reddit.com/r/LocalLLaMA/comments/155vy0k/llama_2_too_repetitive/ (And maybe also related to this topic: https://arize.com/blog/lost-in-the-middle-how-language-models-use-long-contexts-paper-reading/ and https://github.com/THUDM/LongBench )
Also: I've played around a bit with llama. I haven't had good results with summarizing things whatsoever. Maybe it's not the context length, but the wrong model for the task? Aren't there other language models out there, specifically suited for the task of summarization? Llama is kind of generalist and maybe just not exceptionally good at this specific task.
https://huggingface.co/learn/nlp-course/chapter7/5?fw=tf#models-for-text-summarization and https://www.width.ai/post/bart-text-summarization
Regarding the original question: I'm not sure whether KoboldCPP does it correctly for the newer 4k context length. For me it says
Using automatic RoPE scaling (scale:1.000, base:32000.0)But is that the correct base value? That's the same as if i were using an LLaMA1 model with artificially increased context length.You are supposed to manually set scale to 1.0 and base to 10000 when using llama 2 with 4096 context. The automatic scaling assumes the model was trained for 2048. Though as I say in the OP, that still doesn't work, at least with this particular fine tune.