初始化项目，由ModelHub XC社区提供模型
Model: SylvanL/ChatTCM-7B-SFT Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- medical
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license: apache-2.0
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datasets:
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- SylvanL/Traditional-Chinese-Medicine-Dataset-SFT
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language:
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- zh
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base_model:
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- SylvanL/ChatTCM-7B-Pretrain
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pipeline_tag: text-generation
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---
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### 正在写英文论文？
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### 请支持一下作者的最新产品 👉 www.thesisagent.ai
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### 海外学子的AI学术工具， 提供从日常写作到科研论文的全方位辅助。
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### 邀请码：N91M8BE33
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---
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# 国内首个从数据集到训练方式到模型权重完全开源的中医大模型.
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在2张A800-80G上,
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基于SylvanL/ChatTCM-7B-Pretrain, 在llamafactory框架上,
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使用SylvanL/Traditional-Chinese-Medicine-Dataset-SFT进行了2个epoch的全参数量有监督微调(full Supervised Fine-tuning).
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在不出现明显指令丢失或灾难性遗忘的前提下，使模型具备以下能力：
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1. 具有将文言文/古文翻译为现代文的能力, 以加强对于中医典籍的理解与使用.
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2. 具有向主流派别执业医生靠拢的临床诊断逻辑与推方能力, 可以理解输入的患者情况并进行判断与分析.
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3. 具有良好的中医知识问答能力, 可以针对中医领域的知识点进行全面且可靠的解答.
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4. 加强模型面向中医术语的基础nlp能力, 可以更好的赋能如中医命名实体识别, 关系抽取, 关联性分析, 同义实体消岐, 拼写检查与纠错等通用功能.
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P.S.: 模型并没有进行任何identify的植入
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可选Instruction:
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```
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将输入的古文翻译成现代文。
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请为输入的现代文找到其对应的古文原文与出处。
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基于输入的患者医案记录，直接给出你的证型诊断，无需给出原因。
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基于输入的患者医案记录，直接给出你的疾病诊断，无需给出原因。
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基于输入的患者医案记录，直接给出你认为的方剂中药组成。
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基于输入的患者医案记录，直接给出你认为的【治疗方案】{可多选}∈["中药", "成药", "方剂"]，和【诊断】{可多选}∈["证型", "治法", "西医诊断", "中医诊断"]：
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```
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```
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epoch 1:
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"num_input_tokens_seen": 1649269888,
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"total_flos": 3298213988794368.0,
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"train_loss": 1.0691444667014194,
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"train_runtime": 587389.2072,
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"train_samples_per_second": 3.483,
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"train_steps_per_second": 0.016
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epoch 2:
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"num_input_tokens_seen": 1649269888,
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"total_flos": 3298213988794368.0,
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"train_loss": 0.7717254485568724,
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"train_runtime": 600800.1758,
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"train_samples_per_second": 3.406,
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"train_steps_per_second": 0.015
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```
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```
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llamafactory-cli train \
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--stage sft \
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--do_train True \
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--model_name_or_path {SylvanL/ChatTCM-7B-Pretrain} \
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--preprocessing_num_workers 16 \
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--finetuning_type full \
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--template default \
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--flash_attn auto \
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--dataset_dir {dataset_dir} \
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--dataset SFT_medicalKnowledge_source1_548404,SFT_medicalKnowledge_source2_99334,SFT_medicalKnowledge_source3_556540,SFT_nlpDiseaseDiagnosed_61486,SFT_nlpSyndromeDiagnosed_48665,SFT_structGeneral_310860,SFT_structPrescription_92896,_SFT_traditionalTrans_1959542.json,{BAAI/COIG},{m-a-p/COIG-CQIA} \
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--cutoff_len 1024 \
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--learning_rate 5e-05 \
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--num_train_epochs 2.0 \
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--max_samples 1000000 \
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--per_device_train_batch_size 28 \
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--gradient_accumulation_steps 4 \
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--lr_scheduler_type cosine \
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--max_grad_norm 1.0 \
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--logging_steps 1 \
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--save_steps 1000 \
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--warmup_steps 0 \
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--optim adamw_torch \
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--packing False \
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--report_to none \
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--output_dir {output_dir} \
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--bf16 True \
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--plot_loss True \
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--ddp_timeout 180000000 \
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--include_num_input_tokens_seen True \
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--deepspeed cache/ds_z3_offload_config.json
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```
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all_results.json
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all_results.json
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config.json
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"temperature": 0.7,
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"top_p": 0.8,
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20
special_tokens_map.json
Normal file
20
special_tokens_map.json
Normal file
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|
||||
{
|
||||
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|
||||
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|
||||
}
|
||||
3
tokenizer.json
Normal file
3
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Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
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|
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44
tokenizer_config.json
Normal file
44
tokenizer_config.json
Normal file
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|
||||
{
|
||||
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|
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9
train_results.json
Normal file
9
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Normal file
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|
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|
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||||
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|
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9135
trainer_log.jsonl
Normal file
9135
trainer_log.jsonl
Normal file
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Load Diff
73115
trainer_state.json
Normal file
73115
trainer_state.json
Normal file
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Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
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||||
size 7224
|
||||
BIN
training_loss_epoch1.png
Normal file
BIN
training_loss_epoch1.png
Normal file
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|
After Width: | Height: | Size: 42 KiB |
BIN
training_loss_epoch2.png
Normal file
BIN
training_loss_epoch2.png
Normal file
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|
After Width: | Height: | Size: 41 KiB |
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user