---
license: agpl-3.0
---

---
library_name: peft
base_model: models/meta-llama_Meta-Llama-3-8B
---

# Model Card for Model ID

Cyber Threat Intelligence (CTI) fine-tune LoRA Adapter.

Trained on [the ORKL-cleaned-small](https://huggingface.co/datasets/ctitools/orkl-cleaned-small) dataset.



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Aaron Kaplan, Juergen Brandl ([AI Security SIG at FIRST](https://www.first.org/global/sigs/ai-security/))
- **Funded by [optional]:** unfortunately no one
- **Model type:** LLama-3-8B-instruct
- **License:** AGPL-3.0
- **Finetuned from model [optional]:** [meta-llama-llama-3-8b](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

Use this at your own risk, but apart from that...
it's a LoRA that you can stack on top of either other LoRAs and/or on top of meta-llama/llama-3-8b-instruct.

## losses etc.

![training_graph.png](training_graph.png)

### Downstream Use [optional]

CTI texts.

### 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

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[More Information Needed]

### 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.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### 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]

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## Evaluation

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### 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]

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

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## 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

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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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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.8.2