Description
通过ChatGLM-Efficient-Tuning项目微调了chatglm2-6b，并通过该项目的export方式导出merged后的模型chapi
修改config中对应的信息，进行加载，报如下warning, 加载成功后，发现微调的效果并不起效。而通过ChatGLM-Efficient-Tuning调用微调后的模型是起效的。
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warner(
Some weights of the model checkpoint at /data2/model/chapi were not used when initializing ChatGLMForConditionalGeneration: ['lm_head.weight']
- This IS expected if you are initializing ChatGLMForConditionalGeneration from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing ChatGLMForConditionalGeneration from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
2023-08-16 14:24:55 | INFO | model_worker | Register to controller
2023-08-16 14:24:56 | INFO | controller | Register a new worker: http://127.0.0.1:20002
2023-08-16 14:24:56 | INFO | controller | Register done: http://127.0.0.1:20002, {'model_names': ['chapi'], 'speed': 1, 'queue_length': 0}
2023-08-16 14:24:56 | INFO | stdout | INFO: 127.0.0.1:52242 - "POST /register_worker HTTP/1.1" 200 OK
2023-08-16 14:24:56 | ERROR | stderr | INFO: Started server process [1426]
2023-08-16 14:24:56 | ERROR | stderr | INFO: Waiting for application startup.
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我做的是self_cognation的微调，让它输出自己的名字是查派
而直接通过AutoModel调用虽然也是报相同的warning，却能输出微调后预期的结果
from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("/data2/model/chapi", trust_remote_code=True) model = AutoModel.from_pretrained("/data2/model/chapi", trust_remote_code=True).half().cuda() model = model.eval() response, history = model.chat(tokenizer, "你好", history=[]) print(response)
输出：您好！我是 查派，由 xxx开发，旨在为用户提供智能化的回答和支持。