Comments (6)
使用下面这段代码可以正常推理吗?
from transformers import AutoModel, AutoTokenizer
model_path = '/tmp/merged_qlora_model_4bit'
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
input_text = '类型#裙*版型#显瘦*风格#文艺*风格#简约*图案#印花*图案#撞色*裙下摆#压褶*裙长#连衣裙*裙领型#圆领'
response, history = model.chat(tokenizer=tokenizer, query=input_text)
print(response)
from chatglm-6b-qlora.
使用下面这段代码可以正常推理吗?
from transformers import AutoModel, AutoTokenizer model_path = '/tmp/merged_qlora_model_4bit' tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda() input_text = '类型#裙*版型#显瘦*风格#文艺*风格#简约*图案#印花*图案#撞色*裙下摆#压褶*裙长#连衣裙*裙领型#圆领' response, history = model.chat(tokenizer=tokenizer, query=input_text) print(response)
不行,只能推理原来的模型,微调后merge的不行,也是报Loading checkpoint shards: 0%| Killed
from chatglm-6b-qlora.
你的机器环境和依赖包的版本分别是什么样的?
from chatglm-6b-qlora.
Win11 WSL2 Python3.10.12
RTX 4060Ti 16G
依赖包版本除了peft==0.4.0和bitsandbytes==0.41.1外与requirements.txt一致
from chatglm-6b-qlora.
我把bitsandbytes升级到0.41.1貌似也没有问题,我不太能确定是不是windows的平台的问题,我自己的环境是ubuntu上的docker容器,加载起来是这样的:
In [1]: from transformers import AutoModel, AutoTokenizer
In [2]: model_path = '/tmp/t1fp'
In [3]: tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
...: model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:10<00:00, 3.42s/it]
from chatglm-6b-qlora.
求问ChatGLM2-6B,我用数据集微调后,使用adapter推理成功了,但是merge之后使用官方cli_demo会直接Loading checkpoint shards: 0%| Killed,看了一下fp32合并后的模型有23.2G,换成fp16后的模型为11.6G,但是同样会出现killed问题。 训练配置:
{ "output_dir": "saved_files/chatGLM_6B_QLoRA_t32", "per_device_train_batch_size": 4, "gradient_accumulation_steps": 8, "per_device_eval_batch_size": 4, "learning_rate": 1e-3, "num_train_epochs": 1.0, "lr_scheduler_type": "linear", "warmup_ratio": 0.1, "logging_steps": 100, "save_strategy": "steps", "save_steps": 500, "evaluation_strategy": "steps", "eval_steps": 500, "optim": "adamw_torch", "fp16": false, "remove_unused_columns": false, "ddp_find_unused_parameters": false, "seed": 42 }
训练命令:
python3 train_qlora.py --train_args_json chatGLM_6B_QLoRA.json --model_name_or_path chatglm2-6b --train_data_path data/train.jsonl --eval_data_path data/dev.jsonl --lora_rank 4 --lora_dropout 0.05 --compute_dtype fp32
合并命令:
python3 merge_lora_and_quantize.py --lora_path QLoRA_20230811_2500 --output_path output_merged/QLoRA_20230811_2500 --remote_scripts_dir remote_scripts/chatglm2-6b --device auto --qbits 0
你好,我想问一下,lora和qlora微调与初始模型合并方法有什么区别,需要修改什么参数吗?
from chatglm-6b-qlora.
Related Issues (20)
- 数据集格式instruction,input,output时tokenized出错 HOT 2
- 训练结束后,数据展示出现问题
- 调优后的模型是否可以在CPU服务上部署? HOT 1
- protobuf 和 icetk 版本不兼容 HOT 1
- ImportError: libcudnn.so.8: cannot open shared object file: No such file or directory HOT 12
- RuntimeError: mat1 and mat2 shapes cannot be multiplied (588x4096 and 1x9437184) ,ChatGLM2-6B做微调,请问如何设置参数?? HOT 1
- RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:1 and cuda:0! (when checking argument for argument target in method wrapper_CUDA_nll_loss_forward),请问如何调整训练代码,适配多卡训练?? HOT 1
- transformers>4.30.2时,模型不会进行量化,是因为什么?
- BrokenPipeError: [Errno 32] Broken pipe HOT 7
- 训练结束后没有保存完整的adapter文件 HOT 2
- 用V100显卡微调chatglm2-6b,但是loss一直为0,eval_loss=nan
- 训练显存有些问题
- 微调后推理性能问题 HOT 1
- ChatGLM3-6B 微调报错 RuntimeError: CUDA error: invalid argument HOT 1
- ChatGLM2-6B微调后合并报错We need an `offload_dir` to dispatch this model according to this `device_map`, the following submodules need to be offloaded: HOT 2
- 合并模型的时候显存不够,用的4090,24G
- qlora微调训练随机性
- ValueError:
- ChatGLM2-6b模型合并时报错ValueError: We need an `offload_dir` to dispatch this model according to this `device_map`, the following submodules need to be offloaded
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from chatglm-6b-qlora.