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---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
A model fine-tuned on Norwegian prompt/response pairs relevant to the curriculum in radation physics, radation protection and radiological technology for dentistry and dental hygiene students.
It is an experimental model not yet stable enough to use in production.
## Model Details
### Model Description
## Model
The base model used is the Meta Llama 13B model ([meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)).
## Data
A dataset of prompt/response pairs about radiation protection, radiation physics, radiation biology and radiological technology as the apply in dental clinics was used to fine-tune the model. The dataset is in Norwegian and the model is fine-tuned to answer in Norwegian.
## Training
The model was trained on 6.2k prompt/response pairs from the dataset [meta-llama/Llama-2-13b-hf](https://huggingface.co/datasets/geraldOslo/RadProtDataSet) for 6 epochs on a Google Colag notebook with an A100 GPU.
The [Unsloth library](https://github.com/unslothai/unsloth) was used to train the model on a single A100 GPU.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** Gerald Torgersen
- **Model type:** Chat model fine-tuned
- **Language(s) (NLP):** Norwegian
- **License:** Llama 2
- **Finetuned from model [meta-llama/Llama-2-13b-hf]:**
### Model Sources [optional]
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## Uses
For teaching and learning.
### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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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
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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
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#### Factors
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### Results
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#### Summary
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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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