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{
"input_ids": [2,29,29871,2],
"attention_mask": [1,1,1,1]
}
Replace 10777 with the token ID of . eg 29.
“sentence-transformers”: We used the model in the sentence-transformers library.
In addition to performing models, we also provide dozens of example execution languages with python scripts and text on these models
(You can view the online version [here](./multitask_model/example_tasks.md) or view it locally after downloading the repository).
### 1. Install lmdeploy and prepare llama-7B-hf model with git-lfs.
“`shell
pip install lmdeploy
git-lfs install
git clone https://huggingface.co/meta-llama/Llama-2-7b-hf
“`
### 2. Install requirements.
“`shell
pip install sentencepiece einops gradio
“`
### 3. Download Multi Task LoRA parameters with git-lfs.
“`shell
git clone https://huggingface.co/wenge-research/yayi-7b-lora
“`
### 4. Clone this repository.
“`
git clone https://github.com/wenge-research/yayi.git
“`
### 5. run gradio demo with web GUI.
“`
python3 ./lmdeploy_serve_gradio.py –model_path Llama-2-7b-hf –lora_path {path_to_lora_parameters}
“`
Web Demo example:
### 6. run lmdeploy and interact with CLI.
“`
python3 ./lmdeploy_serve_cli.py –model_path Llama-2-7b-hf –lora_path {path_to_lora_parameters}
“`
## 📊 Data construction
In order to better and faster improve the performance of the YAYI model, we will collect different types of high-quality datasets and continue to train the YAYI model.
The following table lists high-quality instruction datasets used by YAYI-7B, and we will open up some datasets processed by our clean data process in the near future.
The [CMMLU]( https://github.com/haonan-li/CMMLU), [C-Eval]( https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Gaokao]( https://github.com/OpenLMLab/GAOKAO-Bench) datasets used in evaluation have been mask out.
|Task Type|Link|Dataset Name|Language|Size|
|:—:|:—:|:—:|:—:|:—:|
|Mutil-Choice and Question Answering|[Link]( https://github.com/OpenLMLab/GAOKAO-Bench/tree/main/data/multiple_choice), [Link](https://github.com/Universal-Guardian/GAOKAO-Bench/tree/main/data/subjective), [Link](https://github.com/FranxYao/chain-of-thought-hub/tree/main/datasets)|AQuA-RAT、DROP、RACE(High)、RACE(Middle)、Gaokao-EN、Gaokao-MATH(En)、Gaokao-CHI(EN)、C3、CommonsenseQA、OpenBookQA、QASC、QASPER、HeadQA-en、NaturalQuestions、QuAC、BoolQ、SQuAD1.1、SQuAD2.0、CoQA、TriviaQA、SAMSum、TS |English|4.3M|
|Mutil-Choice and Question Answering|[Link](https://github.com/haonan-li/CMMLU/tree/master/data), [Link](https://github.com/ymcui/Chinese-LLaMA-Alpaca), [Link](https://github.com/OpenLMLab/GAOKAO-Bench/tree/main/data/multiple_choice), [Link](https://github.com/Universal-Guardian/GAOKAO-Bench/tree/main/data/subjective)|CMNLI、OCNLI、CMRC、DRCD、C3、ChID、csl、eprstmt、tnews、csldcp、cluewsc、iflytek、Gaokao-MATH、Gaokao-CHI-GK、Gaokao-CHI-Cot|Chinese|7.6M|
|Rewriting|[Link](https://github.com/OptimalScale/LMFlow/tree/main/data/self_instruct), [Link](https://github.com/jianzhnie/Chinese-ChatLLaMA/tree/main/data/ChatLM_Data), [Link](https://github.com/databranch/Open-Chinese-ChatLLaMA/tree/main/data/ChatLM_Data)|ADGEN、Alpaca-GPT-4Data、GPT4all、GPTeacher、HC3-Chinese、Prosocial-Dialogue、ruozhiba、self_instruct|Bilingual|7.7K|
|Generation|[Link](https://github.com/OptimalScale/LMFlow/tree/main/data/self_instruct)|ADGEN、Alpaca-GPT-4(zh)、baike、COIG、cpm_2022、 EducaDg-chatgpt、GuanacoDataset_ShareGPT、lima、 oasst1、Open-Assistant、sharegpt、trans_chinese_alpaca_data|Bilingual|5.2K|
|Code|[Link](https://github.com/labring/laf)|code_alpaca|Bilingual|106.5K| ## 📝 Limitations Our work has the following limitations: 1. Pre-training dataset may not cover all minority cultural perspectives in Chinese, leading to some cultural biases. 2. The model may generate incorrect responses when faced with ambiguous or unclear inputs. In addition, complex reasoning ability can still be improved. These limitations provide Valuable insights for future research and development to improve multi-task learning model and promote an inclusive AI ecosystem. ## License YAYI-7B is under the Apache 2.0 license, while the use of the Ya Yi model weights must comply with the Model License. Data that cannot be covered by Apache 2.0 and Model License are requested to contact the owner or follow the open source agreement and bear corresponding responsibilities and obligations. ## 📎 Citation @misc{yayi, title={YaYi:New Open-Source and Foundation LLMs in Chinese and English}, ## Declarations Our team may change the open source agreement of this project at any time in the future without notifying users relevant information before change. ## 🤝 Acknowledgement We sincerely thankall users for their valuable feedback and suggestions on the YAYI model.At the same time, we would like to thank the following institutions for their support and contributions to this project, including but not limited to computing resources, data collection, evaluation and research: 2023 Peking University under the Sichuan Sichuan Society 2023 Zhongke Haizhi Institute for Artificial Intelligence Research Institute of Beijing Open Source Chip Research Institute 2023 Beijing Zhiyuan Artificial Intelligence Research Institute ### Star History
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Successful. simulift. A full-stack app development framework combining RabbitMQ in a fast, accurate, and easy to use fashion. A complete project including project management, design project methodology, and tutorials.
The main front end simulates a dock-free smart elevator. First, you have to move the elevators, make their assignments, and start the elevator. At that point, you place the application and simulate queuing people entering and exiting the elevator.
Using Express.js backend, Docker Compose, and Docker-Swarm you can handle front end company staff technical activities.
Queueing system and smart elevators stimulate the same use case.
I created it because I run into problems when new projects start.
What makes the simulator unique is its simple OneMax engine in the back end to simplify things. Other tools may not optimize the number of rooms or transit requirements. These differences are what set simlulift apart.
OneMax schedules tours, monitors trips, and reduces the amount of time people stay on the elevator.
This simulator also does not require manual building of all the elevator floors. You set the floor plans and the simulation operates automatically.
The simulator uses Angular JS, has a backend using Express (NodeJS), and can use virtual devices to simulate hardware. Express is used to capture REST API content.
A project delivered in docker-compose with docker swarm includes: the API server, the dispatcher, the scheduler, and the driver. mongodb.json is used to store elevator events and tours.
In order to run you have to use the following prerequisites:
RabbitMQ installed and running.
Docker compose installed and running.
Running a docker-compose.yml file
At the end of this document is an example used to launch all the containers. You can also add more features to them.
Running Map: introduction
All jobs over the same occupation are simulated. For example, in silk itinerant professions, mechanics and clerks. For each occupation, the duration and type of works are established; some of those occupations, moreover, involve intensive manual work, in which case, the duration of the process will be shown in frame ‘Critical Works’.
You can start your transition with a simulated planning system and an elevator. This planning system and routing system determine assignment levels, by flexibly minimizing waiting time. There are tactics for continuous work, client loading, permanent occupation, closed loop maintenance etc.
If you want to describe what it is like to work in this vehicle, then use the phone.
“Hello, this is Jon Smith calling from #Itinerants group. I have an urgent job, was there any itinerary available? ”
See here the picture of the Itinerants.
The managers currently select the itinerary; they based their choice upon history statistics. However, using LTS and NLP algorithms, elevator location zoning can reflect usage, giving customers from restricted localities priority.
When the elevator has been selected and the tour chosen to be used, an Itinerant is created and assigned to the elevator. The simulated planning system manages tours, and dispatches based on the itinerary selected.
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