Comments (10)
Same here, it seems that the new import structure breaks the transformers on the Windows platform: f24f084
https://github.com/hiyouga/LLaMA-Factory/actions/runs/10809410559/job/29987044325
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Thanks for the report! This should have been fixed here: #33436
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Could you try updating main
and letting me know whether this is fixed on your side?
from transformers.
It fixed the problem for me. Best experience ever, just logged in to see if there's the same issue I have, turns out the fix was submitted a minute ago. Tested it and it works!
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oh wait a second, I just tested it from different environment, it still doesn't work for me
from transformers.
Hey @GregoryBolshakov, thanks for the report! Do you get the same error in that different environment?
In that different environment, can you double check whether the latest main
was fetched and the runtime restarted? Thank you 🙏
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It fixed our CI here: huggingface/trl#2051
So any other issues that aren't in our CI definitely should be. Thanks for your help with the reports!
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sorry for the second comment, it turned out the repository was not pulled on the second machine, I just updated, it works
from transformers.
Fixed here
from transformers.
Fantastic news! Leaving this issue open a bit more in case others come, will close if no new updates.
Thanks all!
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Related Issues (20)
- ValueError: Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation HOT 1
- TypeError: '<' not supported between instances of 'NoneType' and 'int' HOT 2
- Can’t train Mamba2 with FP16 (Mamba(/2)ForCausalLM) HOT 1
- batch inference scales linearly with batch size when input is long HOT 1
- Exception raised with trainer + `accelerate launch` FSDP + large gradient accumulation steps + small dataset
- Do Transformers onnx export support the input of the Llama is the input_embeds? HOT 1
- Cannot batch them ({'num_frames', 'input_features', 'is_last'} != {'input_features', 'is_last'}) HOT 1
- The examples in the examples directory are mostly for fine-tuning pre-trained models?how to trian from scratch
- `gpt2` with `output_attentions=True` has different attentions shape between flash and eager
- Try to convert LlamaForCausalLM to ONNX with input_embeds as input
- eval_loss not found when training a peft model using trainer.py HOT 2
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- Default setting of GenerationConfig
- `Zero-shot object detection` documentation sentence rephrase
- Usage of deprecated task will break CI after datasets-3.0
- Support Pixtral
- [Distilbert] Torch jit trace failed with `load_in_8bit=True`.
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