---
library_name: transformers
tags:
- unsloth
---

# Model Card for `bayrameker/threat_detection_lora`

This LoRA fine-tuned model is designed to identify and generate text related to various defense and security threats. It was trained on a dataset containing examples of different threat categories (e.g., cyber warfare, espionage, disinformation, etc.) in the context of defense industry news or statements.

## Model Details

### Model Description

- **Developed by:** [Bayram Eker (bayrameker)](https://huggingface.co/bayrameker)
- **Finetuned from model:** [unsloth/Phi-4](https://huggingface.co/unsloth/Phi-4)  
- **Model type:** LoRA-based Causal Language Model (decoder-only architecture)
- **Language(s) (NLP):** Primarily Turkish (and some English content, if present in the dataset)
- **License:** *Currently unspecified* (the base model’s license terms may apply)
- **Shared by:** [Bayram Eker (bayrameker)](https://huggingface.co/bayrameker)

This model was LoRA fine-tuned with [Unsloth](https://github.com/unslothai/unsloth) on a curated dataset dealing with defense-related threats, focusing on threat type detection and short descriptive outputs.

### Model Sources

- **Repository (Hub):** [bayrameker/threat_detection_lora](https://huggingface.co/bayrameker/threat_detection_lora)
- **Paper [optional]:** *No dedicated paper at this time*
- **Demo [optional]:** *No public demo at this time*

## Uses

This LoRA model can be used in text generation or chat-like scenarios where the user asks about potential threats in a defense/security context. The model is capable of producing threat categories (e.g., espionage, cyber-attack, disinformation) and short descriptions.

### Direct Use

- **Chatbot / QA assistant** for defense-related threat descriptions.
- **Text generation** around security/defense news, or summarizing threats.

### Downstream Use

- **Threat classification** or **risk analysis** tools, where the model’s generated categories are used as a starting point for further classification pipelines.

### Out-of-Scope Use

- Detailed, real-time intelligence or geostrategic analytics (the model does not guarantee factual correctness or current data).
- Legal, financial, or medical advice.
- Any domain requiring certified, high-stakes decision-making where incorrect predictions could cause harm.

## Bias, Risks, and Limitations

This model was fine-tuned on a relatively specialized dataset focusing on defense-related threats. It may exhibit the following limitations:

- **Hallucination**: The model may invent or exaggerate threat types not present in the data.
- **Cultural / Geographic Bias**: The training data may be more skewed towards certain regions or conflicts.
- **Incomplete or Outdated Info**: The model’s knowledge cutoff depends on the base model and fine-tuning data; it may not reflect the latest developments in defense technology or geopolitics.

### Recommendations

- Do not rely solely on model outputs for critical defense or security-related decisions.
- Cross-verify the model’s threat descriptions with domain experts.
- Be mindful of potential misinterpretations when using the model’s outputs in real-world settings.

## How to Get Started with the Model

Below is a sample code snippet to load and run inference:

```python
from unsloth import FastLanguageModel
from unsloth.chat_templates import get_chat_template

# Load LoRA fine-tuned model from Hugging Face
model_name = "bayrameker/threat_detection_lora"

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_name,
    device_map="auto"
)

tokenizer = get_chat_template(
    tokenizer,
    chat_template="phi-4",
)

FastLanguageModel.for_inference(model)

messages = [
    {"role": "user", "content": "Rusya ile ilgili tehditler"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    input_ids=inputs,
    max_new_tokens=256,
    temperature=0.8,
    min_p=0.2,
    use_cache=True,
)

generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]
print(generated_text)
```

## Training Details

### Training Data

- The dataset used is from [**bayrameker/threat-detection**](https://huggingface.co/datasets/bayrameker/threat-detection), which contains defense-related short texts (e.g., new weapon systems, geopolitical statements) paired with their potential threats (cyber warfare, espionage, etc.).
- The data is primarily in Turkish, with possible bilingual or English content in some entries.

### Training Procedure

- **LoRA Fine-Tuning Framework**: [Unsloth](https://github.com/unslothai/unsloth)
- **Base Model**: [unsloth/Phi-4](https://huggingface.co/unsloth/Phi-4)
- **Hyperparameters**:
  - LoRA rank (`r`): 16
  - LoRA `lora_alpha`: 16
  - `lora_dropout`: 0
  - Mixed-precision: typically bf16 or fp16 (depending on GPU)
  - Learning Rate (LR): ~2e-4
  - Batch Size / Gradient Accum Steps: Varied based on GPU memory
  - Steps/Epochs: Adjusted for the dataset size

#### Speeds, Sizes, Times [optional]
- Dependent on GPU hardware (e.g., NVIDIA A100 or similar).
- No explicit throughput or wall-clock times reported.

## Evaluation

### Testing Data, Factors & Metrics

- **Testing Data**: The same or a subset of [bayrameker/threat-detection](https://huggingface.co/datasets/bayrameker/threat-detection) can be used for evaluation.
- **Factors**: The content includes different security contexts, focusing on “threat_type” variety.
- **Metrics**: Primarily manual or qualitative evaluation (threat categories are short text). A formal metric (accuracy/F1) could be used if the data had clear gold-standard labels.

### Results

Qualitative evaluation shows the model can produce short paragraphs describing potential threats related to a user’s prompt (e.g., “Rusya ile ilgili tehditler”). Exact numeric scores are not reported.

## Model Examination [optional]

No specific interpretability tools were used or documented.

## Environmental Impact

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).  
Exact figures not provided.

## Technical Specifications [optional]

### Model Architecture and Objective

- A LoRA adaptation on a GPT-style language model (decoder-only).
- Objective: Next-token prediction, guided by conversation templates (SFT — Supervised Fine Tuning).

### Compute Infrastructure

- **Hardware**: GPU (e.g., NVIDIA A100, or similar).
- **Software**: PyTorch, transformers, accelerate, [Unsloth library](https://github.com/unslothai/unsloth).

## Citation [optional]

If you use or modify this model, please credit the base model (Phi-4 by Unsloth) and the fine-tuning repository.

```bibtex
@misc{bayramekerThreatDetectionLoRA,
  author = {Eker, Bayram},
  title = {{Threat Detection LoRA}},
  howpublished = {\url{https://huggingface.co/bayrameker/threat_detection_lora}},
  year={2023}
}
```

## Model Card Authors

- [Bayram Eker (bayrameker)](https://huggingface.co/bayrameker)