FIX: timemixer shapes mismatch and doc update #1138
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Sep 4, 2024
carusyte
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Sep 5, 2024
elephaint
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Sep 12, 2024
* Fix issue #950: Reduce TimeLLM setup time for training * Restore changes on the examples * Revert changes to nbs/models.ipynb, nbs/models.softs.ipynb and neuralforecast/_modidx.py * Revert changes to nbs/models.ipynb, nbs/models.softs.ipynb and neuralforecast/_modidx.py * Refactor code to dynamically load models with AutoModel, AutoTokenizer, and AutoConfig - Updated load_model_and_tokenizer function to use AutoModel, AutoTokenizer, and AutoConfig for flexible model loading. - Included default model(gpt2) for cases where the specified model fails to load. - Kept llm, llm_config, and llm_tokenizer arguments to minimize changes. - Changed llm from storing pretrained weights to accepting pretrained model path to reduce necessary modifications. This update enhances the flexibility and reliability of model loading based on received feedback while minimizing necessary changes. * Refactor code to dynamically load models with AutoModel, AutoTokenizer, and AutoConfig - Updated load_model_and_tokenizer function to use AutoModel, AutoTokenizer, and AutoConfig for flexible model loading. - Included default model(gpt2) for cases where the specified model fails to load. - Kept llm, llm_config, and llm_tokenizer arguments to minimize changes. - Changed llm from storing pretrained weights to accepting pretrained model path to reduce necessary modifications. This update enhances the flexibility and reliability of model loading based on received feedback while minimizing necessary changes. * clear output * modify test code * Optimize model loading and add deprecation warning - Simplify model loading logic - Add constant for default model name - Improve error handling for model loading - Add success messages for model loading - Implement deprecation warning for 'llm_config' and 'llm_tokenizer' parameters - Update print messages for clarity - Remove redundant code This commit improves code readability, maintainability, and user experience by providing clearer feedback and warnings about deprecated parameters. * Resolved conflict in nbs/models.timellm.ipynb --------- Co-authored-by: ive2go <[email protected]> Co-authored-by: Olivier Sprangers <[email protected]>
marcopeix
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Sep 16, 2024
* Use math.ceil to prevent shape mismatch * Show exog support for KAN in doc * FEAT: TimeLLM is faster and supports more LLMs (#1139) * Fix issue #950: Reduce TimeLLM setup time for training * Restore changes on the examples * Revert changes to nbs/models.ipynb, nbs/models.softs.ipynb and neuralforecast/_modidx.py * Revert changes to nbs/models.ipynb, nbs/models.softs.ipynb and neuralforecast/_modidx.py * Refactor code to dynamically load models with AutoModel, AutoTokenizer, and AutoConfig - Updated load_model_and_tokenizer function to use AutoModel, AutoTokenizer, and AutoConfig for flexible model loading. - Included default model(gpt2) for cases where the specified model fails to load. - Kept llm, llm_config, and llm_tokenizer arguments to minimize changes. - Changed llm from storing pretrained weights to accepting pretrained model path to reduce necessary modifications. This update enhances the flexibility and reliability of model loading based on received feedback while minimizing necessary changes. * Refactor code to dynamically load models with AutoModel, AutoTokenizer, and AutoConfig - Updated load_model_and_tokenizer function to use AutoModel, AutoTokenizer, and AutoConfig for flexible model loading. - Included default model(gpt2) for cases where the specified model fails to load. - Kept llm, llm_config, and llm_tokenizer arguments to minimize changes. - Changed llm from storing pretrained weights to accepting pretrained model path to reduce necessary modifications. This update enhances the flexibility and reliability of model loading based on received feedback while minimizing necessary changes. * clear output * modify test code * Optimize model loading and add deprecation warning - Simplify model loading logic - Add constant for default model name - Improve error handling for model loading - Add success messages for model loading - Implement deprecation warning for 'llm_config' and 'llm_tokenizer' parameters - Update print messages for clarity - Remove redundant code This commit improves code readability, maintainability, and user experience by providing clearer feedback and warnings about deprecated parameters. * Resolved conflict in nbs/models.timellm.ipynb --------- Co-authored-by: ive2go <[email protected]> Co-authored-by: Olivier Sprangers <[email protected]> * Consistency with math.ceil --------- Co-authored-by: Olivier Sprangers <[email protected]> Co-authored-by: ive2go <[email protected]>
marcopeix
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Sep 18, 2024
* WIP - Add reversible mixture of kan * WIP - Allows import of RMoK * AutoRMoK, add it to doc, add parameters * Fix tests * Get default config of AutoRMoK * FIX: timemixer shapes mismatch and doc update (#1138) * Use math.ceil to prevent shape mismatch * Show exog support for KAN in doc * FEAT: TimeLLM is faster and supports more LLMs (#1139) * Fix issue #950: Reduce TimeLLM setup time for training * Restore changes on the examples * Revert changes to nbs/models.ipynb, nbs/models.softs.ipynb and neuralforecast/_modidx.py * Revert changes to nbs/models.ipynb, nbs/models.softs.ipynb and neuralforecast/_modidx.py * Refactor code to dynamically load models with AutoModel, AutoTokenizer, and AutoConfig - Updated load_model_and_tokenizer function to use AutoModel, AutoTokenizer, and AutoConfig for flexible model loading. - Included default model(gpt2) for cases where the specified model fails to load. - Kept llm, llm_config, and llm_tokenizer arguments to minimize changes. - Changed llm from storing pretrained weights to accepting pretrained model path to reduce necessary modifications. This update enhances the flexibility and reliability of model loading based on received feedback while minimizing necessary changes. * Refactor code to dynamically load models with AutoModel, AutoTokenizer, and AutoConfig - Updated load_model_and_tokenizer function to use AutoModel, AutoTokenizer, and AutoConfig for flexible model loading. - Included default model(gpt2) for cases where the specified model fails to load. - Kept llm, llm_config, and llm_tokenizer arguments to minimize changes. - Changed llm from storing pretrained weights to accepting pretrained model path to reduce necessary modifications. This update enhances the flexibility and reliability of model loading based on received feedback while minimizing necessary changes. * clear output * modify test code * Optimize model loading and add deprecation warning - Simplify model loading logic - Add constant for default model name - Improve error handling for model loading - Add success messages for model loading - Implement deprecation warning for 'llm_config' and 'llm_tokenizer' parameters - Update print messages for clarity - Remove redundant code This commit improves code readability, maintainability, and user experience by providing clearer feedback and warnings about deprecated parameters. * Resolved conflict in nbs/models.timellm.ipynb --------- Co-authored-by: ive2go <[email protected]> Co-authored-by: Olivier Sprangers <[email protected]> * Consistency with math.ceil --------- Co-authored-by: Olivier Sprangers <[email protected]> Co-authored-by: ive2go <[email protected]> * Add image, docstring, fix typo in comment --------- Co-authored-by: Olivier Sprangers <[email protected]> Co-authored-by: ive2go <[email protected]>
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Prevent shape mismatch in TimeMixer
Update docs: KAN supports exog feature, but currently it says that it doesn't support them