---
library_name: transformers
tags:
- trl
- sft
datasets:
- nenad1002/quantum_science_research_dataset
language:
- en
metrics:
- rouge
- bertscore
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
---

# Model Card for Model ID

Quantum Research Bot is a model fined tuned over the latest research data in quantum science. It contains data from the second half of 2024 making it more performant than base models.


## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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### Model Sources [optional]

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## Uses

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### 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

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## Training Details

### Training Data

Initially trained on a bit less than 3k entries, it was later expanded t 5k high quality questions and answers to make the best of supervised fine tuning.

The dataset was generated by crawling the https://quantum-journal.org/ site, and passing data into the OpenAI gpt-4-turbo model with various prompts to ensure high quality data generation.

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### Training Procedure

Many training procedures were tried alongside with multiple models.

After exensive grid search, supervised fine tuning of Llama 3.1-8B with LORA+ resulted in the best training and evaluation cross entropy.

#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed]
- bfloat16 precision
- LORA rank: 8
- LORA alpha: 16
- LORA droput: 0.1
- Unfreezed nodes are attention, MLP, and embeddings
- Optimizer: AdamW
- LR: 1e-4
- LR scheduler: cosine
- NEFT enabled: true
- Batch size: 8
- Number of epochs: 3


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

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## Evaluation


#### Metrics

Since the fine-tuned model is designed to summarize newly learned data, ROUGE and BERTScore metrics were measured on a sample of 50 manually crafted questions. The reference answers were constructed during the creation of the training and evaluation sets. 
Given that GPT-4-turbo was already used in this context, I did not compare my model against it. Instead, I chose to compare it against the following models:

| Metric | quantum-research-bot-v1.0 | Meta-Llama-3.1-8B  | gemini-1.5-pro   |
|:------------------|:---------------------------|:--------------------|:------------------|
| **BERTScore F1**     | 0.5821                    | 0.3305             |    0.4982        |
| **ROUGE-1** | 0.6045       | 0.3152    |0.5029  |
| **ROUGE-2**|  0.4098          | 0.1751    | 0.3104 |
| **ROUGE-L**| 0.5809          |  0.2902    | 0.4856  |



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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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#### Hardware

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#### Software

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## Citation [optional]

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## Glossary [optional]

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## Model Card Authors [optional]

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