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I've tried to finetune the llm4decompile-6.7b model on my dataset and the result is impressive.
My own dataset looks like the following format
```{'instruction': 'MY_CUSTOMIZE_QUESTION, 'input': '',…
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运行报错代码如下：
###infering###
((), (), (), ()) tensor([0, 0, 0, 0], device='cuda:0')
Traceback (most recent call last):
File "/home/pxc/Cornucopia-LLaMA-Fin-Chinese/infer.py", line 168, in
main…
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NOTE: if this is not a bug report, please use the [GitHub Discussions](https://github.com/facebookresearch/esm/discussions) for support questions (How do I do X?), feature requests, ideas, showcasing …
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我是用transformers的trainer类去做的微调训练，每次一到eval的步骤就会报错，信息如下：
AttributeError: Caught AttributeError in replica 1 on device 1.
Original Traceback (most recent call last):
File "/home/uos/miniconda3/envs/l…
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I tried to fine tune the 13B model with a 3090 (24GB Ram). The training was started and a progress bar was also shown, however, I got an error saying 'maximum recursion depth exceeded' after 100 steps…
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Hi, great work! Thanks for sharing!
When I trained the released codes after feeding the weights and data as provided in readme, I encountered an error as follows:
 didn't find any similar reports…
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I saw the hardware requirement for training chat-llama
13B to 20B → 8x Nvidia A100 (80Gb)
but check this article from HF where they show how to do it with a single 4090
https://huggingface.co…
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Trained vicuna-13b-1.1 LORA in 4bit
Now trying to merge it for running generations but it fails with the following error
```
python3.11/site-packages/peft/tuners/lora.py", line 352, in merge_an…