---
license: apache-2.0
language:
- en
- zh
- ko
- ja
- fr
- it
base_model:
- ibm-granite/granite-3.1-3b-a800m-base
library_name: transformers
datasets:
- lianghsun/tw-novel-1.1B
- lianghsun/tw-finance-159M
- lianghsun/tw-legal-news-24M
- lianghsun/tw-gov-news-90M
- lianghsun/tw-gov-556k
- lianghsun/tw-news-551M
- lianghsun/tw-health-43M
- lianghsun/tw-science-24M
- lianghsun/tw-book-43M
- lianghsun/tw-society-88M
- lianghsun/tw-law-article-evolution
- lianghsun/tw-processed-judgments
- lianghsun/tw-legal-methodology
- lianghsun/tw-legal-qa
- lianghsun/tw-judgment-gist
- lianghsun/reasoning-base-20k
- lianghsun/wikipedia-zh-filtered
- AWeirdDev/zh-tw-pts-articles-sm
- bhxiang/c4_calibrate_mini
- intfloat/multilingual_cc_news
tags:
- Taiwan
- ROC
- IBM
- Granite
- zh-tw
---
# Model Card for Marble-3B

<a href="https://discord.gg/fj6WbHMvfs" target="_blank">[👋歡迎加入 Discord 討論🎉]</a>
<!-- Provide a quick summary of what the model is/does. -->
![image/png](https://cdn-uploads.huggingface.co/production/uploads/618dc56cbc345ca7bf95f3cd/Dxhem9QlZ4Y2S8vikmyMi.png)

採用 [ibm-granite/granite-3.1-3b-a800m-base](https://huggingface.co/ibm-granite/granite-3.1-3b-a800m-base) 為[基礎模型（foundation model）](https://en.wikipedia.org/wiki/Foundation_model)，使用大量[中華民國台灣](https://zh.wikipedia.org/zh-tw/%E8%87%BA%E7%81%A3)的繁體中文語料和多國語料進行模型[持績預訓練（continual pretraining, CPT）](https://docs.unsloth.ai/basics/continued-pretraining)，旨在訓練出具有中華民國台灣知識及風格的 [MOE](https://huggingface.co/blog/moe) [小語言模型（small langugae model, SLM）](https://www.ibm.com/think/topics/small-language-models)。

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Huang Liang Hsun](https://www.linkedin.com/in/lianghsunhuang)
- **Model type:** GraniteMoeForCausalLM
- **Language(s) (NLP):** zh-tw
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
- **Continual pre-trained from model:** [ibm-granite/granite-3.1-3b-a800m-base](https://huggingface.co/ibm-granite/granite-3.1-3b-a800m-base)

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [lianghsun/Marble-3B](https://huggingface.co/lianghsun/Marble-3B/)
- **Demo:** [WIP]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors

[Huang Liang Hsun](https://www.linkedin.com/in/lianghsunhuang)

## Model Card Contact

[Huang Liang Hsun](https://www.linkedin.com/in/lianghsunhuang)