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View Code? Open in Web Editor NEWQuick Start for Large Language Models (Theoretical Learning and Practical Fine-tuning) 大语言模型快速入门(理论学习与微调实战)
License: Apache License 2.0
Quick Start for Large Language Models (Theoretical Learning and Practical Fine-tuning) 大语言模型快速入门(理论学习与微调实战)
License: Apache License 2.0
ImportError Traceback (most recent call last)
[<ipython-input-9-10f3d88ac51c>](https://localhost:8080/#) in <cell line: 1>()
----> 1 from awq import AutoAWQForCausalLM
2 from transformers import AutoTokenizer
3
4 model_path = 'facebook/opt-6.7b'
5 quant_path = "/Content/drive/models/opt-6.7b-awq"
3 frames
[/content/AutoAWQ/awq/modules/fused/model.py](https://localhost:8080/#) in <module>
3 from typing import List
4 from awq.utils import fused_utils
----> 5 from transformers.modeling_outputs import BaseModelOutputWithPast, MoeModelOutputWithPast
6 from awq.modules.fused.block import MPTBlock, FalconDecoderLayer, LlamaLikeBlock, MixtralBlock
7
ImportError: cannot import name 'MoeModelOutputWithPast' from 'transformers.modeling_outputs' (/usr/local/lib/python3.10/dist-packages/transformers/modeling_outputs.py)
由于时间原因,只使用了5万的样本进行训练。
训练后,使用原测试集的100条进行trainer.evaluate()得到如下结果。
{'eval_loss': 1.2431399822235107,
'eval_accuracy': 0.57,
'eval_runtime': 1.7855,
'eval_samples_per_second': 56.007,
'eval_steps_per_second': 7.281}
再使用 1000条进行evaluate()得到另一个结果。
{'eval_loss': 1.016939640045166,
'eval_accuracy': 0.64,
'eval_runtime': 15.7258,
'eval_samples_per_second': 63.59,
'eval_steps_per_second': 7.949}
同一个数据集fine-tune完后,用不同数量的样本进行评估,结果有较大偏差,这个可以怎么理解,用100条样本,ACC是0.57,用1000条样本时则升到0.64.但这样来做评估,怎么确定训练数据量用多少为好?
如题。从代码上看是不需要的?
在load_dataset(dataset_name, language_abbr, data_dir="./dataset", split="train", trust_remote_code=True)时报异常:
raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).name})")
ConnectionError: Couldn't reach 'mozilla-foundation/common_voice_11_0' on the Hub (ConnectionError)
AutoGPTQ_transformers 家庭作业
执行 这个的时候:
quant_model67 = AutoModelForCausalLM.from_pretrained(model_id67, quantization_config=quantization_config67, device_map='auto')
报这个错误:
BuilderConfig 'allenai--c4' not found. Available: ['default']
ValueError Traceback (most recent call last)
in <cell line: 2>()
1 tokenizer67 = AutoTokenizer.from_pretrained(model_id67)
----> 2 quant_model67 = AutoModelForCausalLM.from_pretrained(model_id67, quantization_config=quantization_config67, device_map='auto')
9 frames
/usr/local/lib/python3.10/dist-packages/datasets/builder.py in _create_builder_config(self, config_name, custom_features, **config_kwargs)
588 builder_config = self.builder_configs.get(config_name)
589 if builder_config is None and self.BUILDER_CONFIGS:
--> 590 raise ValueError(
591 f"BuilderConfig '{config_name}' not found. Available: {list(self.builder_configs.keys())}"
592 )
ValueError: BuilderConfig 'allenai--c4' not found. Available: ['default']
按照peft_qlora_chatglm.ipynb教程中的代码运行到“加载模型”时报错,提示KeyError: 'inv_freq',具体报错信息如下
peft_qlora_chatglm-加载模型报错.txt
请老师帮分析下,谢谢
作为云服务器的新手, 按照 readme 的文档操作之后发现使用 nohup jupyter lab --port=8000 --NotebookApp.token='替换为你的密码' --notebook-dir=./ &
启动jupyter之后, 终端没有任何提示, 不知道下一步怎么办.
实际上我发现使用云服务有俩个坑:
综上所述: 希望至少在 readme 文档加以说明.
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