Comments (2)
关于第一个问题,可以尝试用目前更新的负样本数量更多的数据集版本,也可以自己尝试定义负样本的个数。关于第二个问题,实际推理的时候batch_size为1,因此是否进行padding对结果不影响
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一般来说,只要经过训练的模型就不可能全出现true的情况,你可以检查一下你的peft库的版本以及checkpoint是否保存下来,peft库存在bug,它的某些版本无法用这套代码保存lora权重,导致模型参数实际上没有更新参数,你可以检查一下你的peft版本以及保存下来的lora权重文件大小是否为0
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Related Issues (20)
- Training-free Reasoning Approaches使用的prompt HOT 1
- 关于模型输出问题 HOT 2
- 微调后的结果无法达到预期 HOT 6
- 您好,请问一下inductive设定是如何实现的 HOT 2
- 您好,请问UMLS的数据集被删除了吗,上传的数据中没找到UMLS的数据集 HOT 1
- Model issues HOT 1
- 关于inductive的一点疑问 HOT 1
- 给定query, 实现头/尾实体预测 HOT 1
- 结果为0.5,0.0,0.0,0.0 HOT 3
- requirements.txt上的git地址没了 HOT 1
- 训练相关问题 HOT 1
- 微调后推理的结果比论文中低很多FB15K-237N数据集+llama7B HOT 4
- wandb: 未找到命令 HOT 3
- 关于推理的问题 HOT 1
- 进行分布式训练时报错 HOT 5
- ValueError: Attempting to unscale FP16 gradients. HOT 2
- safetensors_rust.SafetensorError: Error while deserializing header: InvalidHeaderDeserialization HOT 4
- 寻求文章 HOT 1
- ImportError: cannot import name 'get_env_vars' from 'triton._C.libtriton.triton' (unknown location) HOT 7
- 关于论文表1中的P、R、F1指标的计算。 HOT 4
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