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README.md

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+---
+library_name: transformers
+tags:
+- llama-cpp
+- gguf-my-repo
+base_model:
+- AlicanKiraz0/BaronLLM-llama3.1-v1
+- meta-llama/Llama-3.1-8B-Instruct
+license: mit
+language:
+- en
+pipeline_tag: text-generation
+---
+
+<img src="https://huggingface.co/AlicanKiraz0/SenecaLLM-x-QwQ-32B-Q4_Medium-Version/resolve/main/BaronLLM.png" width="700" />
+
+Finetuned by Alican Kiraz
+
+[![Linkedin](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://tr.linkedin.com/in/alican-kiraz) 
+![X (formerly Twitter) URL](https://img.shields.io/twitter/url?url=https%3A%2F%2Fx.com%2FAlicanKiraz0) 
+![YouTube Channel Subscribers](https://img.shields.io/youtube/channel/subscribers/UCEAiUT9FMFemDtcKo9G9nUQ) 
+
+Links:
+- Medium: https://alican-kiraz1.medium.com/
+- Linkedin: https://tr.linkedin.com/in/alican-kiraz
+- X: https://x.com/AlicanKiraz0
+- YouTube: https://youtube.com/@alicankiraz0
+
+> **BaronLLM** is a large-language model fine-tuned for *offensive cybersecurity research & adversarial simulation*.  
+> It provides structured guidance, exploit reasoning, and red-team scenario generation while enforcing safety constraints to prevent disallowed content.
+
+---
+
+## Run Private GGUFs from the Hugging Face Hub
+
+You can run private GGUFs from your personal account or from an associated organisation account in two simple steps:
+
+1. Copy your Ollama SSH key, you can do so via: `cat ~/.ollama/id_ed25519.pub | pbcopy`
+1. Add the corresponding key to your Hugging Face account by going to your account settings and clicking on “Add new SSH key.”
+
+That’s it! You can now run private GGUFs from the Hugging Face Hub: `ollama run hf.co/{username}/{repository}`.
+
+---
+
+## ✨ Key Features
+
+| Capability | Details |
+|------------|---------|
+| **Adversary Simulation** | Generates full ATT&CK chains, C2 playbooks, and social-engineering scenarios. |
+| **Exploit Reasoning** | Performs step-by-step vulnerability analysis (e.g., SQLi, XXE, deserialization) with code-level explanations. Generation of working PoC code. |
+| **Payload Refactoring** | Suggests obfuscated or multi-stage payload logic **without** disclosing raw malicious binaries. |
+| **Log & Artifact Triage** | Classifies and summarizes attack traces from SIEM, PCAP, or EDR JSON. |
+
+---
+
+
+
+## 🚀 Quick Start
+
+```bash
+pip install "transformers>=4.42" accelerate bitsandbytes
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "AlicanKiraz/BaronLLM-70B"
+
+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)
+
+print(generate("Assess the exploitability of CVE-2024-45721 in a Kubernetes cluster"))
+```
+
+### Inference API
+```python
+from huggingface_hub import InferenceClient
+ic = InferenceClient(model_id)
+ic.text_generation("Generate a red-team plan targeting an outdated Fortinet appliance")
+```
+
+---
+
+## 🏗️ Model Details
+
+| | |
+|---|---|
+| **Base** | Llama-3.1-8B-Instruct |
+| **Seq Len** | 8 192 tokens |
+| **Quantization** | 6-bit variations |
+| **Languages** | EN |
+
+### Training Data Sources *(curated)*
+* Public vulnerability databases (NVD/CVE, VulnDB).
+* Exploit write-ups from trusted researchers (Project Zero, PortSwigger, NCC Group).
+* Red-team reports (with permission & redactions).
+* Synthetic ATT&CK chains auto-generated + human-vetted.
+
+> **Note:** No copyrighted exploit code or proprietary malware datasets were used.  
+> Dataset filtering removed raw shellcode/binary payloads.
+
+### Safety & Alignment
+* **Policy Gradient RLHF** with security-domain SMEs.
+* **OpenAI/Anthropic style policy** prohibits direct malware source, ransomware builders, or instructions facilitating illicit activity.
+* **Continuous red-teaming** via SecEval v0.3.
+
+---
+
+## 📚 Prompting Guidelines
+
+| Goal | Template |
+|------|----------|
+| **Exploit Walkthrough** | "**ROLE:** Senior Pentester\n**OBJECTIVE:** Analyse CVE-2023-XXXXX step by step …" |
+| **Red-Team Exercise** | "Plan an ATT&CK chain (Initial Access → Exfiltration) for an on-prem AD env …" |
+| **Log Triage** | "Given the following Zeek logs, identify C2 traffic patterns …" |
+
+Use `temperature=0.3`, `top_p=0.9` for deterministic reasoning; raise for brainstorming.
+
+
+
+**It does not pursue any profit.**
+
+"Those who shed light on others do not remain in darkness..."

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