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Hi! Do you support exogeneous variables for zero-shot predictions and training from scratch? As I see gluonts supports it like static and dynamic features but not sure this works for lag-llama too
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ataset: traffic Total train points: 12086964 Total val points: 981818
Dataset: kdd_cup_2018_without_missing Total train points: 2925624 Total val points: 307530
Dataset: saugeenday Total train point…
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Hi all,
Thank you for open-sourcing this library.
I am working on the task of anomaly detection in multivariate time series data. I would like to know how to use Lag-Llama to accomplish this. Is…
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Hi, I am very interested in your work on PipeInfer!
However, the current implementation does not seem to support multiple GPUs. Are there any upcoming plans or suggestions for integrating support for…
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Right now we call llama.cpp directly, long-term we should go with either llama.cpp directly or llama-cpp-python. Because maintaining two different llama.cpp backends isn't ideal, they will never be in…
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I'm trying to fine-tune the model using my own data. When using the fine-tuned model to make predictions, I find that lag-llama gives all **zero** value predictions, which does not reproduce the fine-…
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Hi,
I am trying to test Lagllama on a prediction task. the dataset i feed is what i use for context. then using that context, i want it to make predictions. however, the first step of the predictions…
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Hi,
My data is weekly data. As you see here. So I set freq = "7D".
![image](https://github.com/time-series-foundation-models/lag-llama/assets/43529795/98e4a5ab-47a0-4848-8a77-18c227fdae64)
I thin…
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Hello, I wanted to test [madlad400](https://huggingface.co/jbochi/madlad400-3b-mt/blob/main/model-q4k.gguf) which said to be a great translator model.
I downloaded the GGUF and created a file with …
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How to pre-train the lag-llama model with multivariate time series data?
For example:
num_time_steps = 300
data = [
{
"start": pd.Timestamp("2020-01-01", freq="D"),
"targ…