language:
- zh
- en
tags:
- glm
- chatglm
- thudm
Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
Base on ChatGLM2-6B. This model is used to assist in the Sustainability of construction works using BSI Standards.
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- Language(s) (NLP): [More Information Needed]
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Model Sources [optional]
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Uses
Direct Use
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>>> 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, "晚上睡不着应该怎么办", 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.
Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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Glossary [optional]
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Model Card Contact
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