ChatGLM2-6B微调实践
ChatGLM2-6B微调实践
- 环境准备
- 安装部署
- 1、安装 Anaconda
- 2、安装CUDA
- 3、安装PyTorch
- 4、安装 ChatGLM2-6B
- 微调实践
- 1、准备数据集
- 2、安装python依赖
- 3、微调并训练新模型
- 4、微调后模型的推理与评估
- 5、验证与使用微调后的模型
- 微调过程中遇到的问题
环境准备
申请阿里云GPU服务器：
- CentOS 7.6 64
- Anaconda3-2023.07-1-Linux-x86_64
- Python 3.11.5
- GPU NVIDIA A10（显存24 G/1 core）
- CPU 8 vCore/30G
安装部署
1、安装 Anaconda
wget https://repo.anaconda.com/archive/Anaconda3-2023.07-1-Linux-x86_64.sh
sh Anaconda3-2023.07-1-Linux-x86_64.sh
根据提示一路安装即可。
2、安装CUDA
wget https://developer.download.nvidia.com/compute/cuda/11.2.0/local_installers/cuda_11.2.0_460.27.04_linux.run
sh cuda_11.2.0_460.27.04_linux.run
根据提示安装即可
3、安装PyTorch
conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia
如提示找不到conda命令，需配置Anaconda环境变量。
4、安装 ChatGLM2-6B
mkdir ChatGLM
cd ChatGLM
git clone https://github.com/THUDM/ChatGLM2-6B.git
cd ChatGLM2-6B
pip install -r requirements.txt
加载模型，需要从网上下载模型的7个分片文件，总共大约10几个G大小，可提前下载。
模型下载地址：https://huggingface.co/THUDM/chatglm2-6b/tree/main
微调实践
1、准备数据集
准备我们自己的数据集，分别生成训练文件和测试文件这两个文件，放在目录 ChatGLM2-6B/ptuning/myDataset/ 下面。
训练集文件： train_file.json
测试集文件： val_file.json
2、安装python依赖
后面微调训练，需要依赖一些 Python 模块，提前安装一下：
conda install rouge_chinese nltk jieba datasets
3、微调并训练新模型
修改 train.sh 脚本文件，根据自己实际情况配置即可，修改后的配置为：
PRE_SEQ_LEN=128
LR=2e-2
NUM_GPUS=1torchrun --standalone --nnodes=1 --nproc-per-node=$NUM_GPUS main.py \--do_train \--train_file myDataset/train_file.json \--validation_file myDataset/val_file.json \--preprocessing_num_workers 6 \--prompt_column content \--response_column summary \--overwrite_cache \--model_name_or_path /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b \--output_dir output/zhbr-chatglm2-6b-checkpoint \--overwrite_output_dir \--max_source_length 64 \--max_target_length 128 \--per_device_train_batch_size 6 \--per_device_eval_batch_size 6 \--gradient_accumulation_steps 16 \--predict_with_generate \--max_steps 20 \--logging_steps 5 \--save_steps 5 \--learning_rate $LR \--pre_seq_len $PRE_SEQ_LEN \--quantization_bit 4
修改完即可进行微调：
cd /root/ChatGLM/ChatGLM2-6B/ptuning/
sh train.sh
运行结果如下：
(base) [root@iZbp178u8rw9n9ko94ubbyZ ptuning]# sh train.sh
[2023-10-08 13:09:12,312] torch.distributed.run: [WARNING] master_addr is only used for static rdzv_backend and when rdzv_endpoint is not specified.
10/08/2023 13:09:15 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
10/08/2023 13:09:15 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=IntervalStrategy.NO,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=None,
generation_num_beams=None,
gradient_accumulation_steps=16,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=HubStrategy.EVERY_SAVE,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=0.02,
length_column_name=length,
load_best_model_at_end=False,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=output/zhbr-chatglm2-6b-checkpoint/runs/Oct08_13-09-15_iZbp178u8rw9n9ko94ubbyZ,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=5,
logging_strategy=IntervalStrategy.STEPS,
lr_scheduler_type=SchedulerType.LINEAR,
max_grad_norm=1.0,
max_steps=20,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=OptimizerNames.ADAMW_TORCH,
optim_args=None,
output_dir=output/zhbr-chatglm2-6b-checkpoint,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=6,
per_device_train_batch_size=6,
predict_with_generate=True,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=output/zhbr-chatglm2-6b-checkpoint,
save_on_each_node=False,
save_safetensors=False,
save_steps=5,
save_strategy=IntervalStrategy.STEPS,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
sortish_sampler=False,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)
10/08/2023 13:09:16 - WARNING - datasets.builder - Found cached dataset json (/root/.cache/huggingface/datasets/json/default-8e52c57dfec9ef61/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4)
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1379.71it/s]
[INFO|configuration_utils.py:713] 2023-10-08 13:09:16,749 >> loading configuration file /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b/config.json
[INFO|configuration_utils.py:713] 2023-10-08 13:09:16,751 >> loading configuration file /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b/config.json
[INFO|configuration_utils.py:775] 2023-10-08 13:09:16,751 >> Model config ChatGLMConfig {"_name_or_path": "/root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b","add_bias_linear": false,"add_qkv_bias": true,"apply_query_key_layer_scaling": true,"apply_residual_connection_post_layernorm": false,"architectures": ["ChatGLMModel"],"attention_dropout": 0.0,"attention_softmax_in_fp32": true,"auto_map": {"AutoConfig": "configuration_chatglm.ChatGLMConfig","AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration","AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration","AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration","AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"},"bias_dropout_fusion": true,"classifier_dropout": null,"eos_token_id": 2,"ffn_hidden_size": 13696,"fp32_residual_connection": false,"hidden_dropout": 0.0,"hidden_size": 4096,"kv_channels": 128,"layernorm_epsilon": 1e-05,"model_type": "chatglm","multi_query_attention": true,"multi_query_group_num": 2,"num_attention_heads": 32,"num_layers": 28,"original_rope": true,"pad_token_id": 0,"padded_vocab_size": 65024,"post_layer_norm": true,"pre_seq_len": null,"prefix_projection": false,"quantization_bit": 0,"rmsnorm": true,"seq_length": 32768,"tie_word_embeddings": false,"torch_dtype": "float16","transformers_version": "4.32.1","use_cache": true,"vocab_size": 65024
}[INFO|tokenization_utils_base.py:1850] 2023-10-08 13:09:16,752 >> loading file tokenizer.model
[INFO|tokenization_utils_base.py:1850] 2023-10-08 13:09:16,752 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:1850] 2023-10-08 13:09:16,752 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:1850] 2023-10-08 13:09:16,753 >> loading file tokenizer_config.json
[INFO|modeling_utils.py:2776] 2023-10-08 13:09:16,832 >> loading weights file /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b/pytorch_model.bin.index.json
[INFO|configuration_utils.py:768] 2023-10-08 13:09:16,833 >> Generate config GenerationConfig {"_from_model_config": true,"eos_token_id": 2,"pad_token_id": 0,"transformers_version": "4.32.1"
}Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:05<00:00, 1.39it/s]
[INFO|modeling_utils.py:3551] 2023-10-08 13:09:21,906 >> All model checkpoint weights were used when initializing ChatGLMForConditionalGeneration.[WARNING|modeling_utils.py:3553] 2023-10-08 13:09:21,906 >> Some weights of ChatGLMForConditionalGeneration were not initialized from the model checkpoint at /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b and are newly initialized: ['transformer.prefix_encoder.embedding.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[INFO|modeling_utils.py:3136] 2023-10-08 13:09:21,908 >> Generation config file not found, using a generation config created from the model config.
Quantized to 4 bit
input_ids [64790, 64792, 790, 30951, 517, 30910, 30939, 30996, 13, 13, 54761, 31211, 55046, 54766, 36989, 38724, 54643, 31962, 13, 13, 55437, 31211, 30910, 30939, 31201, 54675, 54592, 33933, 31211, 31779, 32804, 51962, 31201, 39510, 57517, 56689, 31201, 48981, 57486, 55014, 31201, 55568, 56528, 55082, 54831, 54609, 54659, 30943, 31201, 35066, 54642, 36989, 31211, 31779, 35066, 54642, 56042, 55662, 31201, 54539, 56827, 31201, 55422, 54639, 55534, 31201, 33576, 57062, 54848, 31201, 55662, 55816, 41670, 39305, 33760, 36989, 54659, 30966, 31201, 32531, 31838, 54643, 31668, 31687, 31211, 31779, 32531, 31838, 33853, 31201, 32077, 43641, 31201, 54933, 55194, 32366, 32531, 49729, 39305, 33760, 36989, 54659, 30972, 31201, 31641, 48655, 31211, 31779, 36293, 54535, 32155, 31201, 45561, 54585, 31940, 54535, 32155, 31201, 54962, 55478, 54535, 32155, 54609, 31641, 31746, 31639, 31123, 32023, 54603, 36989, 55045, 58286, 49539, 31639, 31123, 36128, 33423, 32077, 36989, 31155, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
inputs [Round 1]问：配网故障类别及原因答： 1、外力破坏：包括车辆撞击、树木刮擦、风筝坠落、倒杆断线等；2、季节性故障：包括季节性覆冰、大雾、雨加雪、温度骤变、冰灾等因素导致的线路故障；3、施工质量及技术方面：包括施工质量不良、设备老化、未按规范施工等原因导致的线路故障；4、管理不到位：包括巡视不及时、发现问题后处理不及时、消缺不及时等管理上的问题，导致小故障积攒成大问题，进而引发设备故障。
label_ids [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 30910, 30939, 31201, 54675, 54592, 33933, 31211, 31779, 32804, 51962, 31201, 39510, 57517, 56689, 31201, 48981, 57486, 55014, 31201, 55568, 56528, 55082, 54831, 54609, 54659, 30943, 31201, 35066, 54642, 36989, 31211, 31779, 35066, 54642, 56042, 55662, 31201, 54539, 56827, 31201, 55422, 54639, 55534, 31201, 33576, 57062, 54848, 31201, 55662, 55816, 41670, 39305, 33760, 36989, 54659, 30966, 31201, 32531, 31838, 54643, 31668, 31687, 31211, 31779, 32531, 31838, 33853, 31201, 32077, 43641, 31201, 54933, 55194, 32366, 32531, 49729, 39305, 33760, 36989, 54659, 30972, 31201, 31641, 48655, 31211, 31779, 36293, 54535, 32155, 31201, 45561, 54585, 31940, 54535, 32155, 31201, 54962, 55478, 54535, 32155, 54609, 31641, 31746, 31639, 31123, 32023, 54603, 36989, 55045, 58286, 49539, 31639, 31123, 36128, 33423, 32077, 36989, 31155, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]
labels 1、外力破坏：包括车辆撞击、树木刮擦、风筝坠落、倒杆断线等；2、季节性故障：包括季节性覆冰、大雾、雨加雪、温度骤变、冰灾等因素导致的线路故障；3、施工质量及技术方面：包括施工质量不良、设备老化、未按规范施工等原因导致的线路故障；4、管理不到位：包括巡视不及时、发现问题后处理不及时、消缺不及时等管理上的问题，导致小故障积攒成大问题，进而引发设备故障。
[INFO|trainer.py:565] 2023-10-08 13:09:26,290 >> max_steps is given, it will override any value given in num_train_epochs
[INFO|trainer.py:1714] 2023-10-08 13:09:26,460 >> ***** Running training *****
[INFO|trainer.py:1715] 2023-10-08 13:09:26,460 >> Num examples = 17
[INFO|trainer.py:1716] 2023-10-08 13:09:26,460 >> Num Epochs = 20
[INFO|trainer.py:1717] 2023-10-08 13:09:26,460 >> Instantaneous batch size per device = 6
[INFO|trainer.py:1720] 2023-10-08 13:09:26,460 >> Total train batch size (w. parallel, distributed & accumulation) = 96
[INFO|trainer.py:1721] 2023-10-08 13:09:26,460 >> Gradient Accumulation steps = 16
[INFO|trainer.py:1722] 2023-10-08 13:09:26,460 >> Total optimization steps = 20
[INFO|trainer.py:1723] 2023-10-08 13:09:26,460 >> Number of trainable parameters = 1,835,0080%| | 0/20 [00:00<?, ?it/s]10/08/2023 13:09:26 - WARNING - transformers_modules.chatglm2-6b.modeling_chatglm - `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...
/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.warnings.warn(
{'loss': 0.5058, 'learning_rate': 0.015, 'epoch': 5.0} 25%|██████████████████████████████████████████████▎ | 5/20 [00:21<00:56, 3.77s/it]Saving PrefixEncoder
[INFO|configuration_utils.py:460] 2023-10-08 13:09:47,797 >> Configuration saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-5/config.json
[INFO|configuration_utils.py:544] 2023-10-08 13:09:47,797 >> Configuration saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-5/generation_config.json
[INFO|modeling_utils.py:1953] 2023-10-08 13:09:47,805 >> Model weights saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-5/pytorch_model.bin
[INFO|tokenization_utils_base.py:2235] 2023-10-08 13:09:47,805 >> tokenizer config file saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-5/tokenizer_config.json
[INFO|tokenization_utils_base.py:2242] 2023-10-08 13:09:47,807 >> Special tokens file saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-5/special_tokens_map.json
/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.warnings.warn(
{'loss': 0.2925, 'learning_rate': 0.01, 'epoch': 9.0} 50%|████████████████████████████████████████████████████████████████████████████████████████████ | 10/20 [00:34<00:31, 3.17s/it]Saving PrefixEncoder
[INFO|configuration_utils.py:460] 2023-10-08 13:10:01,413 >> Configuration saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-10/config.json
[INFO|configuration_utils.py:544] 2023-10-08 13:10:01,413 >> Configuration saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-10/generation_config.json
[INFO|modeling_utils.py:1953] 2023-10-08 13:10:01,419 >> Model weights saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-10/pytorch_model.bin
[INFO|tokenization_utils_base.py:2235] 2023-10-08 13:10:01,420 >> tokenizer config file saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-10/tokenizer_config.json
[INFO|tokenization_utils_base.py:2242] 2023-10-08 13:10:01,420 >> Special tokens file saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-10/special_tokens_map.json
/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.warnings.warn(
{'loss': 0.2593, 'learning_rate': 0.005, 'epoch': 13.0} 75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 15/20 [00:48<00:14, 2.93s/it]Saving PrefixEncoder
[INFO|configuration_utils.py:460] 2023-10-08 13:10:15,139 >> Configuration saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-15/config.json
[INFO|configuration_utils.py:544] 2023-10-08 13:10:15,139 >> Configuration saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-15/generation_config.json
[INFO|modeling_utils.py:1953] 2023-10-08 13:10:15,146 >> Model weights saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-15/pytorch_model.bin
[INFO|tokenization_utils_base.py:2235] 2023-10-08 13:10:15,146 >> tokenizer config file saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-15/tokenizer_config.json
[INFO|tokenization_utils_base.py:2242] 2023-10-08 13:10:15,146 >> Special tokens file saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-15/special_tokens_map.json
/root/anaconda3/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.warnings.warn(
{'loss': 0.3026, 'learning_rate': 0.0, 'epoch': 18.0}
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [01:05<00:00, 3.35s/it]Saving PrefixEncoder
[INFO|configuration_utils.py:460] 2023-10-08 13:10:32,333 >> Configuration saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-20/config.json
[INFO|configuration_utils.py:544] 2023-10-08 13:10:32,333 >> Configuration saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-20/generation_config.json
[INFO|modeling_utils.py:1953] 2023-10-08 13:10:32,340 >> Model weights saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-20/pytorch_model.bin
[INFO|tokenization_utils_base.py:2235] 2023-10-08 13:10:32,340 >> tokenizer config file saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-20/tokenizer_config.json
[INFO|tokenization_utils_base.py:2242] 2023-10-08 13:10:32,340 >> Special tokens file saved in output/zhbr-chatglm2-6b-checkpoint/checkpoint-20/special_tokens_map.json
[INFO|trainer.py:1962] 2023-10-08 13:10:32,354 >> Training completed. Do not forget to share your model on huggingface.co/models =){'train_runtime': 65.8941, 'train_samples_per_second': 29.138, 'train_steps_per_second': 0.304, 'train_loss': 0.3400604248046875, 'epoch': 18.0}
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [01:05<00:00, 3.29s/it]
***** train metrics *****epoch = 18.0train_loss = 0.3401train_runtime = 0:01:05.89train_samples = 17train_samples_per_second = 29.138train_steps_per_second = 0.304
4、微调后模型的推理与评估
对微调后的模型进行评估验证，修改 evaluate.sh 脚本中的 checkpoint 目录：
PRE_SEQ_LEN=128
CHECKPOINT=zhbr-chatglm2-6b-checkpoint
STEP=20
NUM_GPUS=1torchrun --standalone --nnodes=1 --nproc-per-node=$NUM_GPUS main.py \--do_predict \--validation_file myDataset/train_file.json \--test_file myDataset/val_file.json \--overwrite_cache \--prompt_column content \--response_column summary \--model_name_or_path /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b \--ptuning_checkpoint ./output/$CHECKPOINT/checkpoint-$STEP \--output_dir ./output/$CHECKPOINT \--overwrite_output_dir \--max_source_length 64 \--max_target_length 64 \--per_device_eval_batch_size 1 \--predict_with_generate \--pre_seq_len $PRE_SEQ_LEN \--quantization_bit 4
对微调后的模型进行推理和评估：
/root/ChatGLM/ChatGLM2-6B/ptuning/
sh evaluate.sh
运行结果如下：
(base) [root@iZbp178u8rw9n9ko94ubbyZ ptuning]# sh evaluate.sh
[2023-10-08 13:19:53,448] torch.distributed.run: [WARNING] master_addr is only used for static rdzv_backend and when rdzv_endpoint is not specified.
10/08/2023 13:19:56 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
10/08/2023 13:19:56 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=True,
do_train=False,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=IntervalStrategy.NO,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=None,
generation_num_beams=None,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=HubStrategy.EVERY_SAVE,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=5e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=./output/zhbr-chatglm2-6b-checkpoint/runs/Oct08_13-19-56_iZbp178u8rw9n9ko94ubbyZ,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=500,
logging_strategy=IntervalStrategy.STEPS,
lr_scheduler_type=SchedulerType.LINEAR,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=OptimizerNames.ADAMW_TORCH,
optim_args=None,
output_dir=./output/zhbr-chatglm2-6b-checkpoint,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=1,
per_device_train_batch_size=8,
predict_with_generate=True,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=./output/zhbr-chatglm2-6b-checkpoint,
save_on_each_node=False,
save_safetensors=False,
save_steps=500,
save_strategy=IntervalStrategy.STEPS,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
sortish_sampler=False,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)
Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-98f5c44ca2dd481e/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 17623.13it/s]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 3012.07it/s]
Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-98f5c44ca2dd481e/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1488.66it/s]
[INFO|configuration_utils.py:713] 2023-10-08 13:19:57,908 >> loading configuration file /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b/config.json
[INFO|configuration_utils.py:713] 2023-10-08 13:19:57,909 >> loading configuration file /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b/config.json
[INFO|configuration_utils.py:775] 2023-10-08 13:19:57,910 >> Model config ChatGLMConfig {"_name_or_path": "/root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b","add_bias_linear": false,"add_qkv_bias": true,"apply_query_key_layer_scaling": true,"apply_residual_connection_post_layernorm": false,"architectures": ["ChatGLMModel"],"attention_dropout": 0.0,"attention_softmax_in_fp32": true,"auto_map": {"AutoConfig": "configuration_chatglm.ChatGLMConfig","AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration","AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration","AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration","AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"},"bias_dropout_fusion": true,"classifier_dropout": null,"eos_token_id": 2,"ffn_hidden_size": 13696,"fp32_residual_connection": false,"hidden_dropout": 0.0,"hidden_size": 4096,"kv_channels": 128,"layernorm_epsilon": 1e-05,"model_type": "chatglm","multi_query_attention": true,"multi_query_group_num": 2,"num_attention_heads": 32,"num_layers": 28,"original_rope": true,"pad_token_id": 0,"padded_vocab_size": 65024,"post_layer_norm": true,"pre_seq_len": null,"prefix_projection": false,"quantization_bit": 0,"rmsnorm": true,"seq_length": 32768,"tie_word_embeddings": false,"torch_dtype": "float16","transformers_version": "4.32.1","use_cache": true,"vocab_size": 65024
}[INFO|tokenization_utils_base.py:1850] 2023-10-08 13:19:57,911 >> loading file tokenizer.model
[INFO|tokenization_utils_base.py:1850] 2023-10-08 13:19:57,911 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:1850] 2023-10-08 13:19:57,911 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:1850] 2023-10-08 13:19:57,911 >> loading file tokenizer_config.json
[INFO|modeling_utils.py:2776] 2023-10-08 13:19:57,988 >> loading weights file /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b/pytorch_model.bin.index.json
[INFO|configuration_utils.py:768] 2023-10-08 13:19:57,989 >> Generate config GenerationConfig {"_from_model_config": true,"eos_token_id": 2,"pad_token_id": 0,"transformers_version": "4.32.1"
}Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:04<00:00, 1.41it/s]
[INFO|modeling_utils.py:3551] 2023-10-08 13:20:02,988 >> All model checkpoint weights were used when initializing ChatGLMForConditionalGeneration.[WARNING|modeling_utils.py:3553] 2023-10-08 13:20:02,988 >> Some weights of ChatGLMForConditionalGeneration were not initialized from the model checkpoint at /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b and are newly initialized: ['transformer.prefix_encoder.embedding.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[INFO|modeling_utils.py:3136] 2023-10-08 13:20:02,989 >> Generation config file not found, using a generation config created from the model config.
Quantized to 4 bit
input_ids [64790, 64792, 790, 30951, 517, 30910, 30939, 30996, 13, 13, 54761, 31211, 55046, 54848, 55623, 55279, 36989, 13, 13, 55437, 31211]
inputs [Round 1]问：配变雷击故障答：
label_ids [64790, 64792, 30910, 55623, 54710, 31921, 55279, 54538, 55046, 38754, 33760, 54746, 32077, 31123, 32023, 33760, 41711, 31201, 32077, 55870, 56544, 35978, 31155]
labels 雷电直接击中配电网线路或设备，导致线路损坏、设备烧毁等问题。
10/08/2023 13:20:06 - INFO - __main__ - *** Predict ***
[INFO|trainer.py:3119] 2023-10-08 13:20:06,946 >> ***** Running Prediction *****
[INFO|trainer.py:3121] 2023-10-08 13:20:06,946 >> Num examples = 2
[INFO|trainer.py:3124] 2023-10-08 13:20:06,946 >> Batch size = 1
[INFO|configuration_utils.py:768] 2023-10-08 13:20:06,949 >> Generate config GenerationConfig {"_from_model_config": true,"eos_token_id": 2,"pad_token_id": 0,"transformers_version": "4.32.1"
}0%| | 0/2 [00:00<?, ?it/s][INFO|configuration_utils.py:768] 2023-10-08 13:20:11,223 >> Generate config GenerationConfig {"_from_model_config": true,"eos_token_id": 2,"pad_token_id": 0,"transformers_version": "4.32.1"
}100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.09it/s]Building prefix dict from the default dictionary ...
10/08/2023 13:20:13 - DEBUG - jieba - Building prefix dict from the default dictionary ...
Dumping model to file cache /tmp/jieba.cache
10/08/2023 13:20:13 - DEBUG - jieba - Dumping model to file cache /tmp/jieba.cache
Loading model cost 0.440 seconds.
10/08/2023 13:20:13 - DEBUG - jieba - Loading model cost 0.440 seconds.
Prefix dict has been built successfully.
10/08/2023 13:20:13 - DEBUG - jieba - Prefix dict has been built successfully.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00, 1.14s/it]
***** predict metrics *****predict_bleu-4 = 10.9723predict_rouge-1 = 44.0621predict_rouge-2 = 11.9047predict_rouge-l = 33.5968predict_runtime = 0:00:06.56predict_samples = 2predict_samples_per_second = 0.305predict_steps_per_second = 0.305
5、验证与使用微调后的模型
方法一：
编写python脚本，加载微调训练后生成的 Checkpoint 路径：
from transformers import AutoConfig, AutoModel, AutoTokenizer
import os
import torch
# 载入Tokenizer
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
config = AutoConfig.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True, pre_seq_len=128)
model = AutoModel.from_pretrained("THUDM/chatglm2-6b", config=config, trust_remote_code=True)
prefix_state_dict = torch.load(os.path.join("./output/zhbr-chatglm2-6b-checkpoint/checkpoint-20", "pytorch_model.bin"))
new_prefix_state_dict = {}
for k, v in prefix_state_dict.items():if k.startswith("transformer.prefix_encoder."):new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)# Comment out the following line if you don't use quantization
model = model.quantize(4) #或者8
model = model.half().cuda()
model.transformer.prefix_encoder.float()
model = model.eval()response, history = model.chat(tokenizer, "配网线路故障有哪些", history=[])
print(response)
方法二：
修改ptuning中的web_demo.sh，根据自己实际情况配置：
PRE_SEQ_LEN=128CUDA_VISIBLE_DEVICES=0 python3 web_demo.py \--model_name_or_path /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b \--ptuning_checkpoint output/zhbr-chatglm2-6b-checkpoint/checkpoint-20 \--pre_seq_len $PRE_SEQ_LEN
执行web_demo.sh，访问http://xxx.xxx.xxx.xxx:7860。
(base) [root@iZbp178u8rw9n9ko94ubbyZ ptuning]# sh web_demo.sh
/root/ChatGLM/ChatGLM2-6B-main/ptuning/web_demo.py:101: GradioDeprecationWarning: The `style` method is deprecated. Please set these arguments in the constructor instead.user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
Loading prefix_encoder weight from output/zhbr-chatglm2-6b-checkpoint/checkpoint-20
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:04<00:00, 1.47it/s]
Some weights of ChatGLMForConditionalGeneration were not initialized from the model checkpoint at /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b and are newly initialized: ['transformer.prefix_encoder.embedding.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Running on local URL: http://127.0.0.1:7860To create a public link, set `share=True` in `launch()`.
微调过程中遇到的问题
报错信息如下：
dataclasses.FrozenInstanceError: cannot assign to field generation_max_length
和
dataclasses.FrozenInstanceError: cannot assign to field generation_num_beams
解决方法：
在main.py文件中注释掉以下代码。
相关文章：
ChatGLM2-6B微调实践
ChatGLM2-6B微调实践 环境准备安装部署1、安装 Anaconda2、安装CUDA3、安装PyTorch4、安装 ChatGLM2-6B 微调实践1、准备数据集2、安装python依赖3、微调并训练新模型4、微调后模型的推理与评估5、验证与使用微调后的模型 微调过程中遇到的问题 环境准备 申请阿里云GPU服务器&…...
YOLOv7独家改进FPN系列：结合新颖的 GhostSlimPAN 范式网络结构，进一步提升检测器性能
💡本篇内容：YOLOv7改进FPN系列：结合新颖的 GhostSlimFPN 范式网络结构，进一步提升检测器性能 重点：🔥🔥🔥YOLOv7 使用这个 核心创新点 在数据集改进做实验：即插即用： 当 Slim 遇到 YOLO 系列 💡🚀🚀🚀本博客 YOLO系列 + 改进源代码改进 适用于 YOLOv7…...
12. Java异常及异常处理处理
Java —— 异常及处理 1. 异常2. 异常体系3. 常见Exception4. 异常处理4.1 try finally catch关键字4.2 throws和throw 自定义异常4.3 finally，final，finalize三者的区别 1. 异常 异常：在程序执行过程中发生的意外状况，可能导致程…...
自定义hooks函数
体会1 1、js文件中定义useXX函数 export function usetestY() {const count ref(10);const doubleCount computed(() > count.value * 2);return {count,doubleCount,}; } 2、在vue文件中使用useXX函数 import { usetestY } from ./data;const { count, doubleCount } …...
Linux系统及Docker安装RabbitMq
目录 一、linux系统安装 1、上传文件 2、在线安装依赖环境 3、安装Erlang 4、安装RabbitMQ 5、开启管理界面及配置 6、启动 7、删除mq 二、docker安装 1、上传mq.tar包或使用命令拉取镜像 2、启动并运行 3、访问mq 一、linux系统安装 1、上传文件 2、在线安装依赖环…...
山东省赛二阶段第一部分解题思路
提交攻击者的IP地址 192.168.1.7 这个直接awk过滤一下ip次数，这个ip多得离谱，在日志里面也发现了它的恶意行为，后门，反弹shell 识别攻击者使用的操作系统 Linux 找出攻击者资产收集所使用的平台 shodan 提交攻击者目…...
WebGoat 靶场 JWT tokens 四 五 七关通关教程
文章目录 webGoat靶场第 四 关 修改投票数第五关第七关 你购买书，让Tom用户付钱 webGoat靶场 越权漏洞 将webgoat-server-8.1.0.jar复制到kali虚拟机中 sudo java -jar webgoat-server-8.1.0.jar --server.port8888解释： java：这是用于执行…...
【单元测试】如何使用 JUnit5 框架？
JUnit5 单元测试框架使用教程 一、Junit5 是什么？ Junit5是一个用于在Java平台上进行单元测试的框架。JUnit 5 框架主要由三部分组成：JUnit Platform、JUnit Jupiter 和 JUnit Vintage。 JUnit Platform：定义了测试引擎的 API，是…...
C#封装、继承和多态的用法详解
大家好，今天我们将来详细探讨一下C#中封装、继承和多态的用法。作为C#的三大面向对象的特性，这些概念对于程序员来说非常重要，因此我们将对每个特性进行详细的说明，并提供相应的示例代码。 目录 1. 封装（Encapsulati…...
数据结构与算法（持续更新）
线性表 单链表 单链表的定义 由于顺序表的插入删除操作需要移动大量的元素，影响了运行效率，因此引入了线性表的链式存储——单链表。单链表通过一组任意的存储单元来存储线性表中的数据元素，不需要使用地址连续的存储单元，因此它…...
HDLbits: ece241 2014 q4
module top_module (input clk,input x,output z ); reg [2:0] Q;always(posedge clk)beginQ[0] < Q[0] ^ x;Q[1] < (~Q[1]) & x;Q[2] < (~Q[2]) | x;z < ~(| Q[2:0]); //错误！！！！endendmodule 正确答案…...
-- gmssl - 国密算法)
LuatOS-SOC接口文档(air780E)-- gmssl - 国密算法
sm.sm2encrypt(pkx,pky,data)# sm2算法加密 参数 传入值类型 解释 string 公钥x,必选 string 公钥y,必选 string 待计算的数据,必选,最长255字节 返回值 返回值类型 解释 string 加密后的字符串, 原样输出,未经HEX转换 例子 local originStr "encryptio…...
【线性代数及其应用 —— 第一章 线性代数中的线性方程组】-1.线性方程组
所有笔记请看： 博客学习目录_Howe_xixi的博客-CSDN博客https://blog.csdn.net/weixin_44362628/article/details/126020573?spm1001.2014.3001.5502思维导图如下： 内容笔记如下：...
vue实现拖拽排序
在业务中列表拖拽排序是比较常见的需求，常见的JS拖拽库有Sortable.js，Vue.Draggable等，大多数同学遇到这种需求也是更多的求助于这些JS库，其实，使用HTML原生的拖放事件来实现拖拽排序并不复杂，结合Vue的tra…...
IS-IS
二、IS-IS中的DIS与OSPF中的DR Level-1和Level-2的DIS是分别选举的，用户可以为不同级别的DIS选举设置不同的优先级。DIS的选举规则如下：DIS优先级数值最大的被选为DIS。如果优先级数值最大的路由器有多台，则其中MAC地址最大的路由器会成为DI…...
【MySQL】为什么使用B+树做索引
MySQL的innoDB引擎使用的是B树的结构来存储索引的，那么为什么会使用B树呢？为什么不使用其他的结构?本篇我们深入MySQL底层来了解B树。本文中说到的MySQL都是InnoDB引擎的 在这之前，先了解一下InnoDB是如何存储数据的 MySQL是根据数据页的方式…...
php 安装mongodb扩展模块，rdkafka模块
mongodb mongodb扩展下载 选择php版本，根据报错提示，选择扩展对应的版本选择非安全进程将php_mongodb.dll放到php/ext目录下修改php.ini配置，添加extensionphp_mongodb.dll开启php_mongodb扩展，重启服务php -m 查看是否开启成功…...
【数据结构】初探时间与空间复杂度：算法评估与优化的基础
🚩纸上得来终觉浅， 绝知此事要躬行。 🌟主页：June-Frost 🚀专栏：数据结构 🔥该文章主要了解算法的时间复杂度与空间复杂度等相关知识。 目录： 🌏 时间复杂度🔭…...
SpringCloud Alibaba - Sentinel 限流规则（案例 + JMeter 测试分析）
目录 一、Sentinel 限流规则 1.1、簇点链路 1.2、流控模式 1.2.1、直接流控模式 1.2.2、关联流控模式 a）在 OrderController 中新建两个端点. b）在 Sentinel 控制台中对订单查询端点进行流控 c）使用 JMeter 进行测试 d）分…...
uniapp 条件编译 APP 、 H5 、 小程序
一、#ifdef、#ifndef、 #endif三者的区别、 标识作用#ifdef仅在某个平台上使用#ifndef在除了这个平台的其他平台上使用(非此平台使用）#endif结束条件编译 二、平台标识 标识平台APP-PLUS5AppMP微信小程序/支付宝小程序/百度小程序/头条小程序/QQ小程序MP-WEIXIN微…...
SCAU期末笔记 - 数据分析与数据挖掘题库解析
这门怎么题库答案不全啊日 来简单学一下子来 一、选择题（可多选） 将原始数据进行集成、变换、维度规约、数值规约是在以下哪个步骤的任务?(C) A. 频繁模式挖掘 B.分类和预测 C.数据预处理 D.数据流挖掘 A. 频繁模式挖掘：专注于发现数据中…...
UDP(Echoserver)
网络命令 Ping 命令 检测网络是否连通 使用方法: ping -c 次数 网址ping -c 3 www.baidu.comnetstat 命令 netstat 是一个用来查看网络状态的重要工具. 语法：netstat [选项] 功能：查看网络状态 常用选项： n 拒绝显示别名&#…...
2025盘古石杯决赛【手机取证】
前言 第三届盘古石杯国际电子数据取证大赛决赛 最后一题没有解出来，实在找不到，希望有大佬教一下我。 还有就会议时间，我感觉不是图片时间，因为在电脑看到是其他时间用老会议系统开的会。 手机取证 1、分析鸿蒙手机检材&#x…...
uniapp微信小程序视频实时流+pc端预览方案
方案类型技术实现是否免费优点缺点适用场景延迟范围开发复杂度WebSocket图片帧定时拍照Base64传输✅ 完全免费无需服务器 纯前端实现高延迟高流量 帧率极低个人demo测试 超低频监控500ms-2s⭐⭐RTMP推流TRTC/即构SDK推流❌ 付费方案 （部分有免费额度&#x…...
NFT模式：数字资产确权与链游经济系统构建
NFT模式：数字资产确权与链游经济系统构建 ——从技术架构到可持续生态的范式革命 一、确权技术革新：构建可信数字资产基石 1. 区块链底层架构的进化 跨链互操作协议：基于LayerZero协议实现以太坊、Solana等公链资产互通，通过零知…...
HDFS分布式存储 zookeeper
hadoop介绍 狭义上hadoop是指apache的一款开源软件 用java语言实现开源框架，允许使用简单的变成模型跨计算机对大型集群进行分布式处理（1.海量的数据存储 2.海量数据的计算）Hadoop核心组件 hdfs（分布式文件存储系统）&a…...
Kafka入门-生产者
生产者 生产者发送流程： 延迟时间为0ms时，也就意味着每当有数据就会直接发送 异步发送API 异步发送和同步发送的不同在于：异步发送不需要等待结果，同步发送必须等待结果才能进行下一步发送。 普通异步发送 首先导入所需的k…...
Windows安装Miniconda
一、下载 https://www.anaconda.com/download/success 二、安装 三、配置镜像源 Anaconda/Miniconda pip 配置清华镜像源_anaconda配置清华源-CSDN博客 四、常用操作命令 Anaconda/Miniconda 基本操作命令_miniconda创建环境命令-CSDN博客...
阿里云Ubuntu 22.04 64位搭建Flask流程（亲测）
cd /home 进入home盘 安装虚拟环境： 1、安装virtualenv pip install virtualenv 2.创建新的虚拟环境： virtualenv myenv 3、激活虚拟环境（激活环境可以在当前环境下安装包） source myenv/bin/activate 此时，终端…...
从实验室到产业：IndexTTS 在六大核心场景的落地实践
一、内容创作：重构数字内容生产范式 在短视频创作领域，IndexTTS 的语音克隆技术彻底改变了配音流程。B 站 UP 主通过 5 秒参考音频即可克隆出郭老师音色，生成的 “各位吴彦祖们大家好” 语音相似度达 97%，单条视频播放量突破百万…...