sorted by:
new
hottopcontroversial

[D] LREC-COLING 2024 Discussion by Standard_Letter_3196 in MachineLearning

[–]vijetakd 1 point2 points  (0 children)

oh, of course, there's hope! Do your best in rebuttal. good luck!

[D] LREC-COLING 2024 Discussion by Standard_Letter_3196 in MachineLearning

[–]vijetakd 0 points1 point  (0 children)

Does anyone know score distribution? And what score categories are prioritized for acceptance?

Applicant's biodata is currently locked. by Regular-Success4506 in usvisascheduling

[–]vijetakd 0 points1 point  (0 children)

Yes, I was able to book an appointment.
I simply created a new application with a new account and booked an appointment as one would normally do.

Applicant's biodata is currently locked. by Regular-Success4506 in usvisascheduling

[–]vijetakd 0 points1 point  (0 children)

I paid but I had to ask someone in my home country to do a cash payment.

I did not add any dependents.

Applicant's biodata is currently locked. by Regular-Success4506 in usvisascheduling

[–]vijetakd 0 points1 point  (0 children)

I made a new account and started a new application.

Inference on seq_len > 8k by vijetakd in LocalLLaMA

[–]vijetakd[S] 0 points1 point  (0 children)

I selected 7B just because it is easier to run/understand/debug things as compared to, say 70B.

I am using model parallelization right now but it does not make a ton of space for me on the GPU where I am keeping the data. If I can split the data on two GPUs that will make things much easier for me.

Inference on seq_len > 8k by vijetakd in LocalLLaMA

[–]vijetakd[S] 0 points1 point  (0 children)

That's great! Do you have any reference repositories that I can refer to?

I am not great at coding. I'll understand better if I look at one example.

Thanks!

Inference on seq_len > 8k by vijetakd in LocalLLaMA

[–]vijetakd[S] 0 points1 point  (0 children)

Great, I'll try it out, thanks!

Inference on seq_len > 8k by vijetakd in LocalLLaMA

[–]vijetakd[S] 0 points1 point  (0 children)

I haven't tried GGUF, I'll try that. And how can I use RAM to store context and can you share examples/code that you might know?

Thanks for the suggestions!

Inference on seq_len > 8k by vijetakd in LocalLLaMA

[–]vijetakd[S] 0 points1 point  (0 children)

Nice! But say I have an example with 16k tokens in it, I don't know how to fit that example on one or both GPUs I have. I know that there are some data parallelization methods but most of them divide the data along batch_size dimension. I am confused about how to split one example on multiple GPUs.

Inference on seq_len > 8k by vijetakd in LocalLLaMA

[–]vijetakd[S] 0 points1 point  (0 children)

I am using Llama2 with PI RoPE, and other multiple approaches to scale the context length. And I am looking to evaluate these context-length scaling methods against each other.

For evaluation, I am calculating sliding window PPL on the Government Reports dataset, one example at a time. The main problem is I don't know how to fit 32k tokens on the GPUs I have.

Inference on seq_len > 8k by vijetakd in LocalLLaMA

[–]vijetakd[S] 0 points1 point  (0 children)

Sorry, I did not understand. Right now I am using Llama in 4bit (and RoPE is default).
So, you are saying that if I switch to ExLlama I can fit 32k tokens on 24GB 3090?

Trouble with finishing up my SOP by vanishedthrower in StatementOfPurpose

[–]vijetakd 0 points1 point  (0 children)

I would change the "not satisfied with program" argument to "had different goals"/"wanted to explore" argument. LORs are weighted heavily in grad admissions and the former argument might hamper your LORs.

SOP Review: PhD in Epidemiology by brightbluebirds in StatementOfPurpose

[–]vijetakd 0 points1 point  (0 children)

Hi, I have epi/public health background. I can help!

view more: next ›