Comments (4)
请问训练效果如何,按照这种数据标签训练。
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训练出来的结果也是一个列表@ 。@,不过看起来是学到了东西的至少。。。
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@drenched9 是不是可以这样配置你的数据,组成一种多轮对话的方式进行训练呢。
#[
{
"from": "user",
"value": "Picture 1: 1.jpeg\n描述一下这张图片"
},
{
"from": "assistant",
"value": "这是一只猫"
},
{
"from": "user",
"value": "这是猫在干什么?"
},
{
"from": "assistant",
"value": "它正蹲在草地上"
},
{
"from": "user",
"value": "这只猫是什么颜色的?"
},
{
"from": "assistant",
"value": "这只猫是白色的"
},
]
此外的话还想咨询一下,您是用的什么模型呢?是否是chat呢?全量还是lora呢?
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@drenched9 是不是可以这样配置你的数据,组成一种多轮对话的方式进行训练呢。 #[ { "from": "user", "value": "Picture 1: 1.jpeg\n描述一下这张图片" }, { "from": "assistant", "value": "这是一只猫" }, { "from": "user", "value": "这是猫在干什么?" }, { "from": "assistant", "value": "它正蹲在草地上" }, { "from": "user", "value": "这只猫是什么颜色的?" }, { "from": "assistant", "value": "这只猫是白色的" }, ] 此外的话还想咨询一下,您是用的什么模型呢?是否是chat呢?全量还是lora呢?
确实是可以按照你说的方式编写,但是我是想着用一个模板生成,这样太费人力了。
我用的是README上提到的Qwen-VL-Chat-Int4,因为显存原因,跑不了Qwen-VL-Chat。用的是Q-lora微调
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Related Issues (20)
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