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Sponsored by
PoYo.AI
blockblockblock / Mengzi3-13B-Base-bpw6
Model's Last Updated: April 11 2024
text-generation
Introduction of Mengzi3-13B-Base-bpw6
Model Details of Mengzi3-13B-Base-bpw6
Mengzi3-13B-Base
🤗 Hugging Face | 🤖 ModelScope | | Wisemodel ｜ 💬 WeChat | API | 孟子GPT
模型介绍/Introduction
本次开源Mengzi3 13B系列模型，模型的地址如下:
Mengzi3-13B模型基于Llama架构，语料精选自网页、百科、社交、媒体、新闻，以及高质量的开源数据集。通过在万亿tokens上进行多语言语料的继续训练，模型的中文能力突出并且兼顾多语言能力。
Mengzi3-13B is based on the Llama architecture, and the corpus is selected from web pages, encyclopedias, social networking, media, news, and high-quality open source data sets. By continuing to train multilingual corpus on trillions of tokens, the model has outstanding Chinese capabilities and takes into account multilingual capabilities.
快速开始/Quickstart
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Langboat/Mengzi3-13B-Base", use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Langboat/Mengzi3-13B-Base", device_map="auto", trust_remote_code=True)
inputs = tokenizer('指令：回答以下问题。输入：介绍一下孟子。输出：', return_tensors='pt')
if torch.cuda.is_available():
inputs = inputs.to('cuda')
pred = model.generate(**inputs, max_new_tokens=512, repetition_penalty=1.01, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(pred[0], skip_special_tokens=True))
详细的模型推理和微调代码见 Github
Detailed code of model reasoning and finetune see Github
性能评测/Evaluation
Mengzi3-13B-Base在各项基准测试中与同等参数量大语言模型相比，语言能力成绩领先，数学和编程能力位于前列。
Mengzi3-13B-Base leads in language proficiency and is at the forefront in math and programming proficiency compared to the equivalent large language model in various benchmark tests.
| MMLU | CMMLU | OCNLI | GSM8K | HumanEval | |
|---|---|---|---|---|---|
| Baichuan2-13B-Base | 0.530 | 0.489 | 0.433 | 0.528 | 0.171 |
| Qwen-14B | 0.589 | 0.539 | 0.550 | 0.613 | 0.323 |
| ChatGLM3-6B-base | 0.551 | 0.495 | 0.754 | 0.723 | - |
| InternLM2-20B | 0.610 | 0.538 | 0.650 | 0.761 | 0.488 |
| Skywork-13B-base | 0.557 | 0.524 | 0.426 | 0.558 | - |
| LingoWhale-8B | 0.541 | 0.495 | 0.352 | 0.550 | 0.329 |
| DeepSeek-7B | 0.436 | 0.424 | 0.356 | 0.174 | 0.262 |
| DeepSeek-MoE-16B-base | 0.423 | 0.388 | 0.342 | 0.188 | 0.268 |
| MindSource-7B | 0.498 | 0.425 | 0.528 | - | - |
| Mengzi3-13B-Base | 0.651 (+6.7%) | 0.588 (+9.1%) | 0.776 (+2.9%) | 0.631 | 0.287 |
以上结果基于5-shot，MMLU/CMMLU/OCNLI结果来自 FlagEval
The above results are based on 5-shot，MMLU/CMMLU/OCNLI results from FlagEval
协议/License Agreement
Mengzi3-13B-Base依照Apache 2.0协议开源，对学术研究完全开放，同时支持免费商用。如需申请商业许可证，请 联系我们 ，其他商务合作请联系 [email protected] 。
Mengzi3-13B-Base is open source under the Apache 2.0 protocol, fully open for academic research, and free for commercial use. If you need to apply for business license, please contact us , other business cooperation, please contact [email protected] .
Runs of blockblockblock Mengzi3-13B-Base-bpw6 on huggingface.co
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More Information About Mengzi3-13B-Base-bpw6 huggingface.co Model
Url of Mengzi3-13B-Base-bpw6
Mengzi3-13B-Base-bpw6 huggingface.co Url
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