Update README.md

README.md

@@ -1,6 +1,11 @@
 ---
 base_model: mistralai/Mistral-7B-v0.3
 library_name: peft
+license: mit
+datasets:
+- AlicanKiraz0/Cybersecurity-Dataset-v1
+language:
+- en
 ---
 
 # Model Card for Model ID
@@ -13,190 +18,102 @@ library_name: peft
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model is a fine-tuned version of mistralai/Mistral-7B-v0.3, adapted specifically for cybersecurity-related tasks using the AlicanKiraz0/Cybersecurity-Dataset-v1. The model has been further trained using supervised fine-tuning with LoRA (Low-Rank Adaptation) to enhance its ability to answer questions and generate content relevant to information security, threat analysis, incident response, application security, and other cybersecurity domains.
 
+The training objective was to make the base model more effective for real-world cybersecurity use cases, including both offensive and defensive security topics. The fine-tuned model is suitable for tasks such as question answering, incident response simulation, CVE summarization, and security education.
 
+Base Model: mistralai/Mistral-7B-v0.3
+Fine-Tuning Approach: Parameter-efficient fine-tuning with LoRA (PEFT)
+Domain: Cybersecurity (offensive & defensive, information security, vulnerability analysis, incident response, etc.)
+Data: Publicly available, expert-curated cybersecurity texts and structured records
+Intended Use:
+This model is designed to assist cybersecurity professionals, researchers, and educators with high-quality responses and reasoning in the cybersecurity domain. It can be used for chatbots, research assistants, automated knowledge extraction, and educational tools.
 
-- **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]
+## Limitations:
 
-### Model Sources [optional]
+The model may generate outdated or incorrect information if the data is out of date.
+Should not be relied upon for critical, real-world incident response without human oversight.
+Not suitable for generating or promoting illegal or unethical hacking activities.
 
-<!-- Provide the basic links for the model. -->
+## Model type / Language(s) / License / Finetuned from
+Model type: Causal Language Model (Decoder-only, LoRA fine-tuned)
+Language(s): English (en)
+License: MIT
+Finetuned from: mistralai/Mistral-7B-v0.3
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Direct Use
+The model can be used as a conversational assistant or question-answering system for cybersecurity-related topics. Intended users are cybersecurity professionals, students, and researchers.
 
-## Uses
+## Downstream Use
+Can be integrated into educational tools, automated incident response simulators, or red-team/blue-team training assistants.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+## Out-of-Scope Use
+Should not be used for automating actual attack scenarios, generating exploit code, or in critical security systems without human oversight.
 
 ## Bias, Risks, and Limitations
+Model may reflect biases present in public cybersecurity datasets. It may hallucinate or return outdated information and is not a substitute for professional judgment.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
+## How to Get Started
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
 
-### Training Data
+base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.3", device_map="auto", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.3")
+lora_model = PeftModel.from_pretrained(base_model, "[your-hf-username]/[your-model-name]")
 
-<!-- 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. -->
+prompt = "User: What is a buffer overflow?\nAssistant:"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = lora_model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-[More Information Needed]
 
-### Training Procedure
+## Training Data
+See [AlicanKiraz0/Cybersecurity-Dataset-v1](https://huggingface.co/datasets/AlicanKiraz0/Cybersecurity-Dataset-v1) for details and license.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### 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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+## Training Procedure & Hyperparameters
+Precision: fp16 mixed precision
+Batch size: 24
+Epochs: 52
+Learning rate: 2e-4
+LoRA rank (r): 8
+LoRA alpha: 16
 
 ## 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]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+Evaluated on a held-out subset of the same dataset using cross-entropy loss (final value: ~0.81). Human inspection suggests strong security-domain alignment, but thorough downstream task evaluation is ongoing.
 
 ## Environmental Impact
+**Hardware Type:** NVIDIA A100
+**Hours used:** 2
+**Cloud Provider:** Google Colab
 
-<!-- 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
-
-[More Information Needed]
-
-### Compute Infrastructure
+## Technical Specifications
+**Architecture:** Mistral-7B, decoder-only transformer
+**Fine-tuning library:** PEFT 0.15.2 (LoRA)
+**Software:** transformers, peft, bitsandbytes, datasets
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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. -->
+## Model Card Authors [optional]
 
-[More Information Needed]
+[eademir](https://huggingface.co/eademir)
 
-## More Information [optional]
+## Model Card Contact
 
-[More Information Needed]
+https://huggingface.co/eademir
 
-## Model Card Authors [optional]
+## Citation
 
-[More Information Needed]
+If you use this model, please cite it as:
 
-## Model Card Contact
+```bibtex
+@misc{eademir_mistral_cybersec_2024,
+  title        = {Mistral-7B-v0.3 Fine-tuned on Cybersecurity-Dataset-v1},
+  author       = {Eray Aydemir},
+  howpublished = {\url{https://huggingface.co/eademir/mistralv0.3-cybersec}},
+  year         = {2024},
+  note         = {Fine-tuned Mistral-7B-v0.3 on AlicanKiraz0/Cybersecurity-Dataset-v1}
+}
 
-[More Information Needed]
 ### Framework versions
 
 - PEFT 0.15.2