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README.md

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+
 ---
 library_name: transformers
 tags:
 - unsloth
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# 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
 
-<!-- Provide a longer summary of what this model is. -->
+- **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 is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+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.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+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
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+- **Chatbot / QA assistant** for defense-related threat descriptions.
+- **Text generation** around security/defense news, or summarizing threats.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+- **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
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- 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 section is meant to convey both technical and sociotechnical limitations. -->
+This model was fine-tuned on a relatively specialized dataset focusing on defense-related threats. It may exhibit the following limitations:
 
-[More Information Needed]
+- **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
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further 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
 
-Use the code below to get started with the model.
+Below is a sample code snippet to load and run inference:
 
-[More Information Needed]
+```python
+from unsloth import FastLanguageModel
+from unsloth.chat_templates import get_chat_template
 
-## Training Details
+# Load LoRA fine-tuned model from Hugging Face
+model_name = "bayrameker/threat_detection_lora"
 
-### Training Data
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_name,
+    device_map="auto"
+)
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+tokenizer = get_chat_template(
+    tokenizer,
+    chat_template="phi-4",
+)
 
-[More Information Needed]
+FastLanguageModel.for_inference(model)
 
-### Training Procedure
+messages = [
+    {"role": "user", "content": "Rusya ile ilgili tehditler"}
+]
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
 
-#### Preprocessing [optional]
+outputs = model.generate(
+    input_ids=inputs,
+    max_new_tokens=256,
+    temperature=0.8,
+    min_p=0.2,
+    use_cache=True,
+)
 
-[More Information Needed]
+generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]
+print(generated_text)
+```
 
+## Training Details
 
-#### Training Hyperparameters
+### Training Data
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- 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.
 
-#### Speeds, Sizes, Times [optional]
+### Training Procedure
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- **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
 
-[More Information Needed]
+#### Speeds, Sizes, Times [optional]
+- Dependent on GPU hardware (e.g., NVIDIA A100 or similar).
+- No explicit throughput or wall-clock times reported.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **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
 
-[More Information Needed]
-
-#### Summary
-
-
+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]
 
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+No specific interpretability tools were used or documented.
 
 ## 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]
+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
 
-[More Information Needed]
+- 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
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
+- **Hardware**: GPU (e.g., NVIDIA A100, or similar).
+- **Software**: PyTorch, transformers, accelerate, [Unsloth library](https://github.com/unslothai/unsloth).
 
 ## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+If you use or modify this model, please credit the base model (Phi-4 by Unsloth) and the fine-tuning repository.
 
-## Model Card Authors [optional]
+```bibtex
+@misc{bayramekerThreatDetectionLoRA,
+  author = {Eker, Bayram},
+  title = {{Threat Detection LoRA}},
+  howpublished = {\url{https://huggingface.co/bayrameker/threat_detection_lora}},
+  year={2023}
+}
+```
 
-[More Information Needed]
+## Model Card Authors
 
-## Model Card Contact
+- [Bayram Eker (bayrameker)](https://huggingface.co/bayrameker)
 
-[More Information Needed]