---
language:
  - zh
  - en
tags:
  - glm
  - chatglm
  - thudm
---
# Model Card for Model ID

This model is used to assist in the Sustainability of construction works using BSI Standards.

## Model Details

### Model Description

This model is trained using ChatGLM2-6B as the basic model. This model is trained with the content of BSI Standards. The BSI Standards include "Sustainability of construction works — Assessment of environmental performance of buildings — Calculation method" (BS EN 15978:2011) and "Sustainability of construction works – Environmental
product declarations – Core rules for the product category of construction products" (BS EN 15804:2012+A2:2019).

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### Model Sources [optional]

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## Uses

```ipython
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("kkkgg/Chat-SCW", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("kkkgg/Chat-SCW", trust_remote_code=True).half().cuda()
>>> model = model.eval()
>>> response, history = model.chat(tokenizer, "Hello", history=[])
>>> print(response)
Hello! How can I assist you today?
>>> response, history = model.chat(tokenizer, "Explan the life cycle stages of a build system.", history=history)
>>> print(response)
The life cycle stages of a build system encompass the entire life of a construction product or service. These stages encompass the following stages:
Material extraction and processing
Manufacturing and processing
Distribution and transportation
End-of-life and disposal
Construction and renovation
Refurbishment and repair
Maintenance and repair
Renewal and replacement of components
End-of-life and disposal
It is important to note that some stages may overlap or be interconnected, and the specific stages involved may vary depending on the type of construction product or service.
```

## Bias, Risks, and Limitations

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## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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