Update README.md

README.md

@@ -1,51 +1,208 @@
----
-license: mit
-tags:
-- llama-cpp
-- gguf-my-repo
-base_model: AlicanKiraz0/Qwen3-14B-BaronLLM-v2
----
+---
+license: mit
+base_model:
+- Qwen/Qwen3-14B
+tags:
+- llama-cpp
+- gguf-my-repo
+datasets:
+- Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset
+- Trendyol/All-CVE-Chat-MultiTurn-1999-2025-Dataset
+---
 
-# AlicanKiraz0/Qwen3-14B-BaronLLM-v2-Q4_0-GGUF
-This model was converted to GGUF format from [`AlicanKiraz0/Qwen3-14B-BaronLLM-v2`](https://huggingface.co/AlicanKiraz0/Qwen3-14B-BaronLLM-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
-Refer to the [original model card](https://huggingface.co/AlicanKiraz0/Qwen3-14B-BaronLLM-v2) for more details on the model.
+<div align="left">
 
-## Use with llama.cpp
-Install llama.cpp through brew (works on Mac and Linux)
+![Model Type](https://img.shields.io/badge/Model-Cybersecurity%20Specialized-red) ![Base Model](https://img.shields.io/badge/Base%20Model-Qwen3--14B-blue) ![Quantization](https://img.shields.io/badge/Quantization-Q8__0%20GGUF-green) ![License](https://img.shields.io/badge/License-Apache%202.0-yellow) ![Language](https://img.shields.io/badge/Language-English-orange)
 
-```bash
-brew install llama.cpp
+</div>
 
-```
-Invoke the llama.cpp server or the CLI.
+# BaronLLM v2.0 - State-of-the-Art Offensive Security AI Model
 
-### CLI:
-```bash
-llama-cli --hf-repo AlicanKiraz0/Qwen3-14B-BaronLLM-v2-Q4_0-GGUF --hf-file qwen3-14b-baronllm-v2-q4_0.gguf -p "The meaning to life and the universe is"
-```
+<img src="https://huggingface.co/AlicanKiraz0/Qwen3-14B-BaronLLM-v2-Q8/resolve/main/1755245300679.jpeg" width="700" />
 
-### Server:
-```bash
-llama-server --hf-repo AlicanKiraz0/Qwen3-14B-BaronLLM-v2-Q4_0-GGUF --hf-file qwen3-14b-baronllm-v2-q4_0.gguf -c 2048
-```
 
-Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
 
-Step 1: Clone llama.cpp from GitHub.
-```
-git clone https://github.com/ggerganov/llama.cpp
-```
+**Developed Trendyol Group Security Team**
+- Alican Kiraz
+- İsmail Yavuz
+- Melih Yılmaz
+- Mertcan Kondur
+- Rıza Sabuncu
+- Özgün Kultekin
 
-Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
-```
-cd llama.cpp && LLAMA_CURL=1 make
-```
+> **BaronLLM v2.0** is a state-of-the-art large language model fine-tuned specifically for *offensive cybersecurity research & adversarial simulation*, achieving breakthrough performance on industry benchmarks while maintaining safety constraints.
 
-Step 3: Run inference through the main binary.
-```
-./llama-cli --hf-repo AlicanKiraz0/Qwen3-14B-BaronLLM-v2-Q4_0-GGUF --hf-file qwen3-14b-baronllm-v2-q4_0.gguf -p "The meaning to life and the universe is"
-```
-or 
-```
-./llama-server --hf-repo AlicanKiraz0/Qwen3-14B-BaronLLM-v2-Q4_0-GGUF --hf-file qwen3-14b-baronllm-v2-q4_0.gguf -c 2048
+---
+
+## 🏆 Benchmark Achievements
+
+### CS-Eval Global Rankings
+- **13th place** globally among all cybersecurity AI models
+- **4th place** among publicly released models in its parameter class
+- Comprehensive average score: **80.93%**
+
+<img src="https://huggingface.co/AlicanKiraz0/Qwen3-14B-BaronLLM-v2-Q8/resolve/main/1755333107271.jpeg" width="500" />
+<img src="https://huggingface.co/AlicanKiraz0/Qwen3-14B-BaronLLM-v2-Q8/resolve/main/1755245300701.jpeg" width="500" />
+<img src="https://huggingface.co/AlicanKiraz0/Qwen3-14B-BaronLLM-v2-Q8/resolve/main/1755245300692.jpeg" width="500" />
+
+### SecBench Performance Metrics
+
+| Category | BaronLLM v2.0 | vs. Industry Leaders |
+|----------|---------------|----------------------|
+| **Standards & Regulations** | **87.2%** | Only 4.3 points behind Deepseek-v3 (671B) - 48× smaller! |
+| **Application Security** | **85.5%** | Just 4.8 points behind GPT-4o (175B) - 12.5× more compact! |
+| **Endpoint & Host** | **88.1%** | Only 1.4 points behind o1-preview (200B) - 14× higher efficiency! |
+| **MCQ Overall** | **86.9%** | Within 2-6% of premium models! |
+
+
+The model has been trained with 4 H100 GPUs for 65 hours.
+
+### Performance Improvements (v1 → v2)
+- Base model performance boosted by **~1.5x** on CyberSec-Eval benchmarks
+- Enhanced with Causal Reasoning and Chain-of-Thought (CoT) capabilities
+---
+
+## ✨ Key Features
+
+| Capability | Details |
+|------------|---------|
+| **Adversary Simulation** | Generates full ATT&CK chains, C2 playbooks, and social-engineering scenarios |
+| **Exploit Reasoning** | Step-by-step vulnerability analysis with code-level explanations and PoC generation |
+| **Payload Optimization** | Advanced obfuscation techniques and multi-stage payload logic |
+| **Threat Intelligence** | Log analysis, artifact triage, and attack pattern recognition |
+| **Cloud-Native Security** | Kubernetes, serverless, and multi-cloud environment testing |
+| **Emerging Threats** | AI/ML security, quantum computing risks, and zero-day research |
+
+---
+
+## 🏗️ Model Architecture
+
+| Specification | Details |
+|--------------|---------|
+| **Base Model** | Qwen3-14B |
+| **Parameters** | 14 Billion |
+| **Context Length** | 8,192 tokens |
+| **Training Data** | 53,202 curated examples |
+| **Domains Covered** | 200+ specialized cybersecurity areas |
+| **Languages** | English |
+| **Fine-tuning Method** | Instruction tuning with CoT |
+
+---
+
+## 📊 Training Dataset
+
+**53,202** meticulously curated instruction-tuning examples covering **200+ specialized cybersecurity domains**:
+
+### Topic Distribution
+- Cloud Security & DevSecOps: 18.5%
+- Threat Intelligence & Hunting: 16.2%
+- Incident Response & Forensics: 14.8%
+- AI/ML Security: 12.3%
+- Network & Protocol Security: 11.7%
+- Identity & Access Management: 9.4%
+- Emerging Technologies: 8.6%
+- Platform-Specific Security: 5.3%
+- Compliance & Governance: 3.2%
+
+### Data Sources (Curated & Redacted)
+- Public vulnerability databases (NVD/CVE, VulnDB)
+- Security research papers (Project Zero, PortSwigger, NCC Group)
+- Industry threat reports (with permissions)
+- Synthetic ATT&CK chains (auto-generated + human-vetted)
+- Conference proceedings (BlackHat, DEF CON, RSA)
+
+> **Note:** No copyrighted exploit code or proprietary malware datasets were used.
+> Dataset filtering removed raw shellcode/binary payloads.
+
+---
+
+## 🚀 Usage & Access
+
+### Quick Start 
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "AlicanKiraz/BaronLLM-v2.0"  # Requires authentication
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+def generate(prompt, **kwargs):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    output = model.generate(**inputs, max_new_tokens=512, **kwargs)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Example usage
+print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))
 ```
+
+---
+
+## 📚 Prompting Best Practices
+
+| Objective | Template | Parameters |
+|-----------|----------|------------|
+| **Exploit Analysis** | `ROLE: Senior Pentester\nOBJECTIVE: Analyze CVE-XXXX...` | `temperature=0.3, top_p=0.9` |
+| **Red Team Planning** | `Generate ATT&CK chain for [target environment]...` | `temperature=0.5, top_p=0.95` |
+| **Threat Hunting** | `Identify C2 patterns in [log type]...` | `temperature=0.2, top_p=0.85` |
+| **Incident Response** | `Create response playbook for [threat scenario]...` | `temperature=0.4, top_p=0.9` |
+
+---
+
+## 🛡️ Safety & Alignment
+
+### Ethical Framework
+- **Policy Gradient RLHF** with security domain experts
+- **OpenAI/Anthropic-style policies** preventing malicious misuse
+- **Continuous red-teaming** via SecEval v0.3
+- **Dual-use prevention** mechanisms
+
+### Responsible Disclosure
+- Model capabilities are documented transparently
+- Access restricted to verified professionals
+- Usage monitoring for compliance
+- Regular security audits
+
+---
+
+## 📖 Academic Publication
+
+The technical paper detailing BaronLLM v2.0's architecture, training methodology, and benchmark results will be available on arXiv within one month. 
+
+---
+
+## 🤝 Contributing & Support
+
+BaronLLM was originally developed to support the Trendyol Group Security Team and has evolved into a state-of-the-art offensive security AI model. We welcome collaboration from the security community:
+
+- **Bug Reports**: Via GitHub Issues
+- **Feature Requests**: Through community discussions
+- **Research Collaboration**: Contact for academic partnerships
+
+---
+
+## ⚖️ License & Disclaimer
+
+**License:** Apache 2.0 (Model weights require separate authorization)
+
+**Important:** This model is designed for authorized security testing and research only. Users must comply with all applicable laws and obtain proper authorization before conducting any security assessments. The developers assume no liability for misuse.
+
+---
+
+## 🌟 Acknowledgments
+
+Special thanks to:
+- Trendyol Group Security Team
+- The open-source security community
+- Academic Cybersecurity research community
+- All contributors and testers
+
+---
+
+*"Those who shed light on others do not remain in darkness..."*
+
+**This project does not pursue any profit.**
+```