bert4torch下载bert4torch源代码下载

bert4torch

其他源码

v0.5.4

下载

bert4torch

Documentation | Torch4keras | Examples | build_MiniLLM_from_scratch | bert4vector

1. 下载安装

安装稳定版

pip install bert4torch

安装最新版

pip install git+https://github.com/Tongjilibo/bert4torch

注意事项：pip包的发布慢于git上的开发版本，git clone注意引用路径，注意权重是否需要转换
测试用例：git clone https://github.com/Tongjilibo/bert4torch，修改example中的预训练模型文件路径和数据路径即可启动脚本
自行训练：针对自己的数据，修改相应的数据处理代码块
开发环境：原使用torch==1.10版本进行开发，现已切换到torch2.0开发，如其他版本遇到不适配，欢迎反馈

2. 功能

LLM模型: 加载chatglm、llama、 baichuan、ziya、bloom等开源大模型权重进行推理和微调，命令行一行部署大模型
核心功能：加载bert、roberta、albert、xlnet、nezha、bart、RoFormer、RoFormer_V2、ELECTRA、GPT、GPT2、T5、GAU-alpha、ERNIE等预训练权重继续进行finetune、并支持在bert基础上灵活定义自己模型
丰富示例：包含llm、pretrain、sentence_classfication、sentence_embedding、sequence_labeling、relation_extraction、seq2seq、serving等多种解决方案
实验验证：已在公开数据集实验验证，使用如下examples数据集和实验指标
易用trick：集成了常见的trick，即插即用
其他特性：加载transformers库模型一起使用；调用方式简洁高效；有训练进度条动态展示；配合torchinfo打印参数量；默认Logger和Tensorboard简便记录训练过程；自定义fit过程，满足高阶需求
训练过程：

功能	bert4torch	transformers	备注
训练进度条	✅	✅	进度条打印loss和定义的metrics
分布式训练dp/ddp	✅	✅	torch自带dp/ddp
各类callbacks	✅	✅	日志/tensorboard/earlystop/wandb等
大模型推理，stream/batch输出	✅	✅	各个模型是通用的，无需单独维护脚本
大模型微调	✅	✅	lora依赖peft库，pv2自带
丰富tricks	✅		对抗训练等tricks即插即用
代码简洁易懂，自定义空间大	✅		代码复用度高, keras代码训练风格
仓库的维护能力/影响力/使用量/兼容性		✅	目前仓库个人维护
一键部署大模型

3. 快速上手

3.1 上手教程

Quick-Start
快速上手教程，教程示例，实战示例
bert4torch介绍(知乎)，bert4torch快速上手(知乎)，bert4torch又双叒叕更新啦(知乎)

3.2 命令行快速部署大模型服务

本地 / 联网加载

# 联网下载全部文件
bert4torch-llm-server --checkpoint_path Qwen2-0.5B-Instruct

# 加载本地大模型，联网下载bert4torch_config.json
bert4torch-llm-server --checkpoint_path /data/pretrain_ckpt/Qwen/Qwen2-0.5B-Instruct --config_path Qwen/Qwen2-0.5B-Instruct

# 加载本地大模型，且bert4torch_config.json已经下载并放于同名目录下
bert4torch-llm-server --checkpoint_path /data/pretrain_ckpt/Qwen/Qwen2-0.5B-Instruct

命令行 / gradio网页 / openai_api

# 命令行
bert4torch-llm-server --checkpoint_path /data/pretrain_ckpt/Qwen/Qwen2-0.5B-Instruct --mode cli

# gradio网页
bert4torch-llm-server --checkpoint_path /data/pretrain_ckpt/Qwen/Qwen2-0.5B-Instruct --mode gradio

# openai_api
bert4torch-llm-server --checkpoint_path /data/pretrain_ckpt/Qwen/Qwen2-0.5B-Instruct --mode openai

命令行聊天示例

4. 版本和更新历史

4.1 版本历史

更新日期	bert4torch	torch4keras	版本说明
20240928	0.5.4	0.2.7	【新功能】增加deepseek系列、MiniCPM、MiniCPMV、llama3.2、Qwen2.5；支持device_map=auto;【修复】修复batch_generate和n>1的bug
20240814	0.5.3	0.2.6	【新功能】增加llama3.1/Yi1.5；自动选择从hfmirror下载；支持命令行参数`bert4torch-llm-server`
20240801	0.5.2	0.2.5	【新功能】chatglm/qwen系列支持function call调用, 增加internlm2系列；【小优化】简化pipeline中chat demo的调用，generate的终止token元素允许为列表, 统一rope_scaling参数名，增加rope衍生类；【bug】修复flash_attn2的推理bug, 修复bart的tie_word_embedding的bug

更多版本

4.2 更新历史

更多历史

5. 预训练权重

预训练模型支持多种代码加载方式

from bert4torch.models import build_transformer_model

# 1. 仅指定config_path: 从头初始化模型结构, 不加载预训练模型
model = build_transformer_model('./model/bert4torch_config.json')

# 2. 仅指定checkpoint_path: 
## 2.1 文件夹路径: 自动寻找路径下的*.bin/*.safetensors权重文件 + 需把bert4torch_config.json下载并放于该目录下
model = build_transformer_model(checkpoint_path='./model')

## 2.2 文件路径/列表: 文件路径即权重路径/列表, bert4torch_config.json会从同级目录下寻找
model = build_transformer_model(checkpoint_path='./pytorch_model.bin')

## 2.3 model_name: hf上预训练权重名称, 会自动下载hf权重以及bert4torch_config.json文件
model = build_transformer_model(checkpoint_path='bert-base-chinese')

# 3. 同时指定config_path和checkpoint_path(本地路径名或model_name排列组合): 
#    本地路径从本地加载，pretrained_model_name会联网下载
config_path = './model/bert4torch_config.json'  # 或'bert-base-chinese'
checkpoint_path = './model/pytorch_model.bin'  # 或'bert-base-chinese'
model = build_transformer_model(config_path, checkpoint_path)

预训练权重链接和bert4torch_config.json

*注：

高亮格式(如bert-base-chinese)的表示可直接build_transformer_model()联网下载
国内镜像网站加速下载
- HF_ENDPOINT=https://hf-mirror.com python your_script.py
- export HF_ENDPOINT=https://hf-mirror.com后再执行python代码
- 在python代码开头如下设置
```
import os
os.environ['HF_ENDPOINT'] = "https://hf-mirror.com"
```

6. 鸣谢

感谢苏神实现的bert4keras，本实现有不少地方参考了bert4keras的源码，在此衷心感谢大佬的无私奉献;
其次感谢项目bert4pytorch，也是在该项目的指引下给了我用pytorch来复现bert4keras的想法和思路。

7. 引用

@misc{bert4torch,
  title={bert4torch},
  author={Bo Li},
  year={2022},
  howpublished={url{https://github.com/Tongjilibo/bert4torch}},
}

8. 其他

Wechat & Star History Chart
微信群人数超过200个（有邀请限制），可添加个人微信拉群

微信号

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附加信息

版本 v0.5.4
类型其他源码
更新时间 2025-04-19
大小 3.42MB
来自于 Github

bert4torch

目录

1. 下载安装

2. 功能

3. 快速上手

3.1 上手教程

3.2 命令行快速部署大模型服务

4. 版本和更新历史

4.1 版本历史

4.2 更新历史

5. 预训练权重

6. 鸣谢

7. 引用

8. 其他

Google Dorks

shepherd

mongo express

hidusbf

Free Algorithms Books

markdownpedia

chat.petals.dev

GPT Prompt Templates

GPTyped

Google Dorks

shepherd

mongo express

Google Dorks

shepherd

mongo express

模型分类	模型名称	权重来源	权重链接/checkpoint_path	config_path
bert	bert-base-chinese	google-bert	`bert-base-chinese`	`bert-base-chinese`
	chinese_L-12_H-768_A-12	谷歌	tf权重 `Tongjilibo/bert-chinese_L-12_H-768_A-12`
	chinese-bert-wwm-ext	HFL	`hfl/chinese-bert-wwm-ext`	`hfl/chinese-bert-wwm-ext`
	bert-base-multilingual-cased	google-bert	`bert-base-multilingual-cased`	`bert-base-multilingual-cased`
	MacBERT	HFL	`hfl/chinese-macbert-base` `hfl/chinese-macbert-large`	`hfl/chinese-macbert-base` `hfl/chinese-macbert-large`
	WoBERT	追一科技	`junnyu/wobert_chinese_base`，`junnyu/wobert_chinese_plus_base`	`junnyu/wobert_chinese_base` `junnyu/wobert_chinese_plus_base`
roberta	chinese-roberta-wwm-ext	HFL	`hfl/chinese-roberta-wwm-ext` `hfl/chinese-roberta-wwm-ext-large` (large的mlm权重是随机初始化)	`hfl/chinese-roberta-wwm-ext` `hfl/chinese-roberta-wwm-ext-large`
	roberta-small/tiny	追一科技	`Tongjilibo/chinese_roberta_L-4_H-312_A-12` `Tongjilibo/chinese_roberta_L-6_H-384_A-12`
	roberta-base	FacebookAI	`roberta-base`	`roberta-base`
	guwenbert	ethanyt	`ethanyt/guwenbert-base`	`ethanyt/guwenbert-base`
albert	albert_zh albert_pytorch	brightmart	`voidful/albert_chinese_tiny` `voidful/albert_chinese_small` `voidful/albert_chinese_base` `voidful/albert_chinese_large` `voidful/albert_chinese_xlarge` `voidful/albert_chinese_xxlarge`	`voidful/albert_chinese_tiny` `voidful/albert_chinese_small` `voidful/albert_chinese_base` `voidful/albert_chinese_large` `voidful/albert_chinese_xlarge` `voidful/albert_chinese_xxlarge`
nezha	NEZHA NeZha_Chinese_PyTorch	huawei_noah	`sijunhe/nezha-cn-base` `sijunhe/nezha-cn-large` `sijunhe/nezha-base-wwm` `sijunhe/nezha-large-wwm`	`sijunhe/nezha-cn-base` `sijunhe/nezha-cn-large` `sijunhe/nezha-base-wwm` `sijunhe/nezha-large-wwm`
	nezha_gpt_dialog	bojone	`Tongjilibo/nezha_gpt_dialog`
xlnet	Chinese-XLNet	HFL	`hfl/chinese-xlnet-base`	`hfl/chinese-xlnet-base`
	tranformer_xl	huggingface	`transfo-xl/transfo-xl-wt103`	`transfo-xl/transfo-xl-wt103`
deberta	Erlangshen-DeBERTa-v2	IDEA	`IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese` `IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese` `IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese`	`IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese` `IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese` `IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese`
electra	Chinese-ELECTRA	HFL	`hfl/chinese-electra-base-discriminator`	`hfl/chinese-electra-base-discriminator`
ernie	ernie	百度文心	`nghuyong/ernie-1.0-base-zh` `nghuyong/ernie-3.0-base-zh`	`nghuyong/ernie-1.0-base-zh` `nghuyong/ernie-3.0-base-zh`
roformer	roformer	追一科技	`junnyu/roformer_chinese_base`	`junnyu/roformer_chinese_base`
	roformer_v2	追一科技	`junnyu/roformer_v2_chinese_char_base`	`junnyu/roformer_v2_chinese_char_base`
simbert	simbert	追一科技	`Tongjilibo/simbert-chinese-base` `Tongjilibo/simbert-chinese-small` `Tongjilibo/simbert-chinese-tiny`
	simbert_v2/roformer-sim	追一科技	`junnyu/roformer_chinese_sim_char_base`，`junnyu/roformer_chinese_sim_char_ft_base`，`junnyu/roformer_chinese_sim_char_small`，`junnyu/roformer_chinese_sim_char_ft_small`	`junnyu/roformer_chinese_sim_char_base` `junnyu/roformer_chinese_sim_char_ft_base` `junnyu/roformer_chinese_sim_char_small` `junnyu/roformer_chinese_sim_char_ft_small`
gau	GAU-alpha	追一科技	`Tongjilibo/chinese_GAU-alpha-char_L-24_H-768`
uie	uie uie_pytorch	百度	`Tongjilibo/uie-base`
gpt	CDial-GPT	thu-coai	`thu-coai/CDial-GPT_LCCC-base` `thu-coai/CDial-GPT_LCCC-large`	`thu-coai/CDial-GPT_LCCC-base` `thu-coai/CDial-GPT_LCCC-large`
	cmp_lm(26亿)	清华	`TsinghuaAI/CPM-Generate`	`TsinghuaAI/CPM-Generate`
	nezha_gen	huawei_noah	`Tongjilibo/chinese_nezha_gpt_L-12_H-768_A-12`
	gpt2-chinese-cluecorpussmall	UER	`uer/gpt2-chinese-cluecorpussmall`	`uer/gpt2-chinese-cluecorpussmall`
	gpt2-ml	imcaspar	torch BaiduYun(84dh)	`gpt2-ml_15g_corpus` `gpt2-ml_30g_corpus`
bart	bart_base_chinese	复旦fnlp	`fnlp/bart-base-chinese` v1.0	`fnlp/bart-base-chinese` `fnlp/bart-base-chinese-v1.0`
t5	t5	UER	`uer/t5-small-chinese-cluecorpussmall` `uer/t5-base-chinese-cluecorpussmall`	`uer/t5-base-chinese-cluecorpussmall` `uer/t5-small-chinese-cluecorpussmall`
	mt5	谷歌	`google/mt5-base`	`google/mt5-base`
	t5_pegasus	追一科技	`Tongjilibo/chinese_t5_pegasus_small` `Tongjilibo/chinese_t5_pegasus_base`
	chatyuan	clue-ai	`ClueAI/ChatYuan-large-v1` `ClueAI/ChatYuan-large-v2`	`ClueAI/ChatYuan-large-v1` `ClueAI/ChatYuan-large-v2`
	PromptCLUE	clue-ai	`ClueAI/PromptCLUE-base`	`ClueAI/PromptCLUE-base`
chatglm	chatglm-6b	THUDM	`THUDM/chatglm-6b` `THUDM/chatglm-6b-int8` `THUDM/chatglm-6b-int4` v0.1.0	`THUDM/chatglm-6b` `THUDM/chatglm-6b-int8` `THUDM/chatglm-6b-int4` `THUDM/chatglm-6b-v0.1.0`
	chatglm2-6b	THUDM	`THUDM/chatglm2-6b` `THUDM/chatglm2-6b-int4` `THUDM/chatglm2-6b-32k`	`THUDM/chatglm2-6b` `THUDM/chatglm2-6b-int4` `THUDM/chatglm2-6b-32k`
	chatglm3-6b	THUDM	`THUDM/chatglm3-6b` `THUDM/chatglm3-6b-32k`	`THUDM/chatglm3-6b` `THUDM/chatglm3-6b-32k`
	glm4-9b	THUDM	`THUDM/glm-4-9b` `THUDM/glm-4-9b-chat` `THUDM/glm-4-9b-chat-1m`	`THUDM/glm-4-9b` `THUDM/glm-4-9b-chat` `THUDM/glm-4-9b-chat-1m`
llama	llama	meta		`meta-llama/llama-7b` `meta-llama/llama-13b`
	llama-2	meta	meta-llama/Llama-2-7b-hf meta-llama/Llama-2-7b-chat-hf meta-llama/Llama-2-13b-hf meta-llama/Llama-2-13b-chat-hf	`meta-llama/Llama-2-7b-hf` `meta-llama/Llama-2-7b-chat-hf` `meta-llama/Llama-2-13b-hf` `meta-llama/Llama-2-13b-chat-hf`
	llama-3	meta	`meta-llama/Meta-Llama-3-8B` `meta-llama/Meta-Llama-3-8B-Instruct`	`meta-llama/Meta-Llama-3-8B` `meta-llama/Meta-Llama-3-8B-Instruct`
	llama-3.1	meta	`meta-llama/Meta-Llama-3.1-8B` `meta-llama/Meta-Llama-3.1-8B-Instruct`	`meta-llama/Meta-Llama-3.1-8B` `meta-llama/Meta-Llama-3.1-8B-Instruct`
	llama-3.2	meta	`meta-llama/Llama-3.2-1B` `meta-llama/Llama-3.2-1B-Instruct` `meta-llama/Llama-3.2-3B` `meta-llama/Llama-3.2-3B-Instruct`	`meta-llama/Llama-3.2-1B` `meta-llama/Llama-3.2-1B-Instruct` `meta-llama/Llama-3.2-3B` `meta-llama/Llama-3.2-3B-Instruct`
	Chinese-LLaMA-Alpaca	HFL		`hfl/chinese_alpaca_plus_7b` `hfl/chinese_llama_plus_7b`
	Chinese-LLaMA-Alpaca-2	HFL		待添加
	Chinese-LLaMA-Alpaca-3	HFL		待添加
	Belle_llama	LianjiaTech	BelleGroup/BELLE-LLaMA-7B-2M-enc	合成说明、`BelleGroup/BELLE-LLaMA-7B-2M-enc`
	Ziya	IDEA-CCNL	IDEA-CCNL/Ziya-LLaMA-13B-v1 IDEA-CCNL/Ziya-LLaMA-13B-v1.1 IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1	`IDEA-CCNL/Ziya-LLaMA-13B-v1` `IDEA-CCNL/Ziya-LLaMA-13B-v1.1`
	vicuna	lmsys	`lmsys/vicuna-7b-v1.5`	`lmsys/vicuna-7b-v1.5`
Baichuan	Baichuan	baichuan-inc	`baichuan-inc/Baichuan-7B` `baichuan-inc/Baichuan-13B-Base` `baichuan-inc/Baichuan-13B-Chat`	`baichuan-inc/Baichuan-7B` `baichuan-inc/Baichuan-13B-Base` `baichuan-inc/Baichuan-13B-Chat`
	Baichuan2	baichuan-inc	`baichuan-inc/Baichuan2-7B-Base` `baichuan-inc/Baichuan2-7B-Chat` `baichuan-inc/Baichuan2-13B-Base` `baichuan-inc/Baichuan2-13B-Chat`	`baichuan-inc/Baichuan2-7B-Base` `baichuan-inc/Baichuan2-7B-Chat` `baichuan-inc/Baichuan2-13B-Base` `baichuan-inc/Baichuan2-13B-Chat`
Yi	Yi	01-ai	`01-ai/Yi-6B` `01-ai/Yi-6B-200K` `01-ai/Yi-9B` `01-ai/Yi-9B-200K`	`01-ai/Yi-6B` `01-ai/Yi-6B-200K` `01-ai/Yi-9B` `01-ai/Yi-9B-200K`
	Yi-1.5	01-ai	`01-ai/Yi-1.5-6B` `01-ai/Yi-1.5-6B-Chat` `01-ai/Yi-1.5-9B` `01-ai/Yi-1.5-9B-32K` `01-ai/Yi-1.5-9B-Chat` `01-ai/Yi-1.5-9B-Chat-16K`	`01-ai/Yi-1.5-6B` `01-ai/Yi-1.5-6B-Chat` `01-ai/Yi-1.5-9B` `01-ai/Yi-1.5-9B-32K` `01-ai/Yi-1.5-9B-Chat` `01-ai/Yi-1.5-9B-Chat-16K`
bloom	bloom	bigscience	`bigscience/bloom-560m` `bigscience/bloomz-560m`	`bigscience/bloom-560m` `bigscience/bloomz-560m`
Qwen	Qwen	阿里云	`Qwen/Qwen-1_8B` `Qwen/Qwen-1_8B-Chat` `Qwen/Qwen-7B` `Qwen/Qwen-7B-Chat` `Qwen/Qwen-14B` `Qwen/Qwen-14B-Chat`	`Qwen/Qwen-1_8B` `Qwen/Qwen-1_8B-Chat` `Qwen/Qwen-7B` `Qwen/Qwen-7B-Chat` `Qwen/Qwen-14B` `Qwen/Qwen-14B-Chat`
	Qwen1.5	阿里云	`Qwen/Qwen1.5-0.5B` `Qwen/Qwen1.5-0.5B-Chat` `Qwen/Qwen1.5-1.8B` `Qwen/Qwen1.5-1.8B-Chat` `Qwen/Qwen1.5-7B` `Qwen/Qwen1.5-7B-Chat` `Qwen/Qwen1.5-14B` `Qwen/Qwen1.5-14B-Chat`	`Qwen/Qwen1.5-0.5B` `Qwen/Qwen1.5-0.5B-Chat` `Qwen/Qwen1.5-1.8B` `Qwen/Qwen1.5-1.8B-Chat` `Qwen/Qwen1.5-7B` `Qwen/Qwen1.5-7B-Chat` `Qwen/Qwen1.5-14B` `Qwen/Qwen1.5-14B-Chat`
	Qwen2	阿里云	`Qwen/Qwen2-0.5B` `Qwen/Qwen2-0.5B-Instruct` `Qwen/Qwen2-1.5B` `Qwen/Qwen2-1.5B-Instruct` `Qwen/Qwen2-7B` `Qwen/Qwen2-7B-Instruct`	`Qwen/Qwen2-0.5B` `Qwen/Qwen2-0.5B-Instruct` `Qwen/Qwen2-1.5B` `Qwen/Qwen2-1.5B-Instruct` `Qwen/Qwen2-7B` `Qwen/Qwen2-7B-Instruct`
	Qwen2-VL	阿里云	`Qwen/Qwen2-VL-2B-Instruct` `Qwen/Qwen2-VL-7B-Instruct`	`Qwen/Qwen2-VL-2B-Instruct` `Qwen/Qwen2-VL-7B-Instruct`
	Qwen2.5	阿里云	`Qwen/Qwen2.5-0.5B` `Qwen/Qwen2.5-0.5B-Instruct` `Qwen/Qwen2.5-1.5B` `Qwen/Qwen2.5-1.5B-Instruct` `Qwen/Qwen2.5-3B` `Qwen/Qwen2.5-3B-Instruct` `Qwen/Qwen2.5-7B` `Qwen/Qwen2.5-7B-Instruct` `Qwen/Qwen2.5-14B` `Qwen/Qwen2.5-14B-Instruct`	`Qwen/Qwen2.5-0.5B` `Qwen/Qwen2.5-0.5B-Instruct` `Qwen/Qwen2.5-1.5B` `Qwen/Qwen2.5-1.5B-Instruct` `Qwen/Qwen2.5-3B` `Qwen/Qwen2.5-3B-Instruct` `Qwen/Qwen2.5-7B` `Qwen/Qwen2.5-7B-Instruct` `Qwen/Qwen2.5-14B` `Qwen/Qwen2.5-14B-Instruct`
InternLM	InternLM	上海人工智能实验室	`internlm/internlm-7b` `internlm/internlm-chat-7b`	`internlm/internlm-7b` `internlm/internlm-chat-7b`
	InternLM2	上海人工智能实验室	`internlm/internlm2-1_8b` `internlm/internlm2-chat-1_8b` `internlm/internlm2-7b` `internlm/internlm2-chat-7b` `internlm/internlm2-20b` `internlm/internlm2-chat-20b`	`internlm/internlm2-1_8b` `internlm/internlm2-chat-1_8b` `internlm/internlm2-7b` `internlm/internlm2-chat-7b`
	InternLM2.5	上海人工智能实验室	`internlm/internlm2_5-7b` `internlm/internlm2_5-7b-chat` `internlm/internlm2_5-7b-chat-1m`	`internlm/internlm2_5-7b` `internlm/internlm2_5-7b-chat` `internlm/internlm2_5-7b-chat-1m`
Falcon	Falcon	tiiuae	`tiiuae/falcon-rw-1b` `tiiuae/falcon-7b` `tiiuae/falcon-7b-instruct`	`tiiuae/falcon-rw-1b` `tiiuae/falcon-7b` `tiiuae/falcon-7b-instruct`
DeepSeek	DeepSeek-MoE	深度求索	`deepseek-ai/deepseek-moe-16b-base` `deepseek-ai/deepseek-moe-16b-chat`	`deepseek-ai/deepseek-moe-16b-base` `deepseek-ai/deepseek-moe-16b-chat`
	DeepSeek-LLM	深度求索	`deepseek-ai/deepseek-llm-7b-base` `deepseek-ai/deepseek-llm-7b-chat`	`deepseek-ai/deepseek-llm-7b-base` `deepseek-ai/deepseek-llm-7b-chat`
	DeepSeek-V2	深度求索	`deepseek-ai/DeepSeek-V2-Lite` `deepseek-ai/DeepSeek-V2-Lite-Chat`	`deepseek-ai/DeepSeek-V2-Lite` `deepseek-ai/DeepSeek-V2-Lite-Chat`
	DeepSeek-Coder	深度求索	`deepseek-ai/deepseek-coder-1.3b-base` `deepseek-ai/deepseek-coder-1.3b-instruct` `deepseek-ai/deepseek-coder-6.7b-base` `deepseek-ai/deepseek-coder-6.7b-instruct` `deepseek-ai/deepseek-coder-7b-base-v1.5` `deepseek-ai/deepseek-coder-7b-instruct-v1.5`	`deepseek-ai/deepseek-coder-1.3b-base` `deepseek-ai/deepseek-coder-1.3b-instruct` `deepseek-ai/deepseek-coder-6.7b-base` `deepseek-ai/deepseek-coder-6.7b-instruct` `deepseek-ai/deepseek-coder-7b-base-v1.5` `deepseek-ai/deepseek-coder-7b-instruct-v1.5`
	DeepSeek-Coder-V2	深度求索	`deepseek-ai/DeepSeek-Coder-V2-Lite-Base` `deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct`	`deepseek-ai/DeepSeek-Coder-V2-Lite-Base` `deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct`
	DeepSeek-Math	深度求索	`deepseek-ai/deepseek-math-7b-base` `deepseek-ai/deepseek-math-7b-instruct` `deepseek-ai/deepseek-math-7b-rl`	`deepseek-ai/deepseek-math-7b-base` `deepseek-ai/deepseek-math-7b-instruct` `deepseek-ai/deepseek-math-7b-rl`
MiniCPM	MiniCPM	OpenBMB	`openbmb/MiniCPM-2B-sft-bf16` `openbmb/MiniCPM-2B-dpo-bf16` `openbmb/MiniCPM-2B-128k` `openbmb/MiniCPM-1B-sft-bf16`	`openbmb/MiniCPM-2B-sft-bf16` `openbmb/MiniCPM-2B-dpo-bf16` `openbmb/MiniCPM-2B-128k` `openbmb/MiniCPM-1B-sft-bf16`
	MiniCPM-V	OpenBMB	`openbmb/MiniCPM-V-2_6` `openbmb/MiniCPM-Llama3-V-2_5`	`openbmb/MiniCPM-V-2_6` `openbmb/MiniCPM-Llama3-V-2_5`
embedding	text2vec-base-chinese	shibing624	`shibing624/text2vec-base-chinese`	`shibing624/text2vec-base-chinese`
	m3e	moka-ai	`moka-ai/m3e-base`	`moka-ai/m3e-base`
	bge	BAAI	`BAAI/bge-large-en-v1.5` `BAAI/bge-large-zh-v1.5` `BAAI/bge-base-en-v1.5` `BAAI/bge-base-zh-v1.5` `BAAI/bge-small-en-v1.5` `BAAI/bge-small-zh-v1.5`	`BAAI/bge-large-en-v1.5` `BAAI/bge-large-zh-v1.5` `BAAI/bge-base-en-v1.5` `BAAI/bge-base-zh-v1.5` `BAAI/bge-small-en-v1.5` `BAAI/bge-small-zh-v1.5`
	gte	thenlper	`thenlper/gte-large-zh` `thenlper/gte-base-zh`	`thenlper/gte-base-zh` `thenlper/gte-large-zh`