dsfsi/mistral-7b-custom_prompt_long_short_31_gpu_days

Model Card for Model ID

The model is a result of fine-tuning Mistral-7B-v0.1 on a down stream task, in low resourced setting. It is able to translate English sentences to Zulu and Xhosa sentrences.

Model Details

Model Description

dsfsi/mistral-7b-custom_prompt_long_short_31_gpu_days, model, was fine-tuned for 31 GPU days from base model mistralai/Mistral-7B-v0.1. The model was fine-tuned in efforts to improve translation task for large language model in regard to low resourced morphologically rich African languages using custom prompt.

• Developed by: Pitso Walter Khoboko, Vukosi Marivate and Joseph Sefara
• Funded by [optional]: University of Pretoria and Data Science For Social Impact
• Shared by [optional]: Pitso Walter Khoboko
• Model type: Sequence-to-sequence model
• Language(s) (NLP): English to Zulu and Xhosa
• License: cc-by-4.0
• Finetuned from model [optional]: mistralai/Mistral-7B-v0.1

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: https://www.sciencedirect.com/science/article/pii/S2666827025000325
• Demo [optional]: [More Information Needed]

Uses

The model can be used to translate Engslih to Zulu and Xhosa. With further improvement it can be used to translate domain specific infromation from English to Zulu and Xhosa, thus it can be used to get research information that was written in English in the agriculture industry to small scale farmers that speak Zulu and Xhosa. Further, it can be used in the Education industry to teach core subjects in native South African langauges thus can improve pupils' performance in the core subjects.

Direct Use

You can download the model, dsfsi/mistral-7b-custom_prompt_long_short_31_gpu_days, and prompt it to translate English sentences to Zulu and Xhosa sentences.

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Downstream Use [optional]

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Out-of-Scope Use

The model, dsfsi/mistral-7b-custom_prompt_long_short_31_gpu_days, will not work well for politically bias prompts, propmts that promote sexual bias and giving it a whole document written in English and prompting it to translate to Zulu or Xhosa.

Bias, Risks, and Limitations

The dataset that was used to train the model still had some Zulu and Xhosa sentences having English words, thus without further fine tuning with clean dataset the model should not be used in an official capacity to transalte Engslih to Xhosa.

Recommendations

We recommend if want to use the model in an official capacity, you further fine-tune the model and on clean dataset for more than 31 GPU days.

How to Get Started with the Model

Use the code below to get started with the model.

Training Details

Training Data

• nwu-ctext/autshumato
• Helsinki-NLP/opus-100
• WMT22

The above datasets were collected individually and used to create a multilingual dataset having English to Zulu and Xhosa sentences.

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed]

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

• bleu: Used to check if the model is translating Zulu and Xhosa words proprely when compared to the fround truth.
• f1:evaluates larger linguistic units such as grammatical chunks and syntactic frames, making it more suitable for languages with complex syntactic structures.
• G-Eva: uses embeddings to capture the contextual and semantic similarity between hypothesis and reference translations

Results

Eng-Zul: BlueScore-20 F1Score-42 G-Eva-92% Eng-Xh: BlueScore-14 F1Score-38 G-Eva-91%

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]

BibTeX:

@article{khoboko2025optimizing, title={Optimizing translation for low-resource languages: Efficient fine-tuning with custom prompt engineering in large language models}, author={Khoboko, Pitso Walter and Marivate, Vukosi and Sefara, Joseph}, journal={Machine Learning with Applications}, volume={20}, pages={100649}, year={2025}, publisher={Elsevier} }

APA:

Khoboko, P. W., Marivate, V., & Sefara, J. (2025). Optimizing translation for low-resource languages: Efficient fine-tuning with custom prompt engineering in large language models. Machine Learning with Applications, 20, 100649.

Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

Pitso Walter Khoboko

Model Card Contact

[email protected] (Pitso Walter Khoboko), [email protected] (Vukosi Marivate), [email protected] (Joseph Sefara)