Instructions to use Jashan887/75_BugTrace_Core_Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jashan887/75_BugTrace_Core_Pro with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Jashan887/75_BugTrace_Core_Pro",
	filename="bugtraceai-core-pro.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Jashan887/75_BugTrace_Core_Pro with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jashan887/75_BugTrace_Core_Pro
# Run inference directly in the terminal:
llama-cli -hf Jashan887/75_BugTrace_Core_Pro

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jashan887/75_BugTrace_Core_Pro
# Run inference directly in the terminal:
llama-cli -hf Jashan887/75_BugTrace_Core_Pro

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Jashan887/75_BugTrace_Core_Pro
# Run inference directly in the terminal:
./llama-cli -hf Jashan887/75_BugTrace_Core_Pro

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Jashan887/75_BugTrace_Core_Pro
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jashan887/75_BugTrace_Core_Pro

Use Docker

docker model run hf.co/Jashan887/75_BugTrace_Core_Pro

LM Studio
Jan
Ollama
How to use Jashan887/75_BugTrace_Core_Pro with Ollama:
```
ollama run hf.co/Jashan887/75_BugTrace_Core_Pro
```

Unsloth Studio new

How to use Jashan887/75_BugTrace_Core_Pro with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jashan887/75_BugTrace_Core_Pro to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jashan887/75_BugTrace_Core_Pro to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jashan887/75_BugTrace_Core_Pro to start chatting

Pi new

How to use Jashan887/75_BugTrace_Core_Pro with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jashan887/75_BugTrace_Core_Pro

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Jashan887/75_BugTrace_Core_Pro"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jashan887/75_BugTrace_Core_Pro with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jashan887/75_BugTrace_Core_Pro

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Jashan887/75_BugTrace_Core_Pro

Run Hermes

hermes

Docker Model Runner
How to use Jashan887/75_BugTrace_Core_Pro with Docker Model Runner:
```
docker model run hf.co/Jashan887/75_BugTrace_Core_Pro
```

Lemonade

How to use Jashan887/75_BugTrace_Core_Pro with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jashan887/75_BugTrace_Core_Pro

Run and chat with the model

lemonade run user.75_BugTrace_Core_Pro-{{QUANT_TAG}}

List all available models

lemonade list

75_BugTrace_Core_Pro / adapters /README.md

Jashan887

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metadata

language:
  - en
  - es
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
tags:
  - bug-bounty
  - security
  - pentesting
  - exploit-generation
  - waf-bypass
  - cybersecurity
  - hacking
model-index:
  - name: BugTraceAI-CORE-v1
    results: []

🛡️ BugTraceAI-CORE v1.0

BugTraceAI-CORE is a specialized Large Language Model (LLM) fine-tuned for high-performance, private, and local cybersecurity operations. Developed specifically for bug hunters, pentesters, and security researchers, it bridges the gap between general-purpose coding assistants and offensive security experts.

🚀 Key Features

Offensive Security Expertise: Fine-tuned on real-world exploit chains, WAF bypasses, and security methodologies.
Local-First Architecture: Designed to run on consumer-grade GPUs (RTX 3060+) with a high-availability fallback for dual-Xeon CPU environments.
2025/2026 Ready: Trained on recent vulnerability write-ups and disclosed reports to ensure relevance against modern 2025/2026 defense systems.
Zero-Downtime MLOps: Integrated with a secondary CPU fallback using llama.cpp for 24/7 availability during re-training cycles.

🧠 Training & Methodology

The model was built using the Unsloth library for optimized QLoRA training on a single RTX 3060 (12GB VRAM).

Datasets (The Hacker's Brain)

WAF Evasion & Injection: Trained on darkknight25/WAF_DETECTION_DATASET for generating payloads that bypass modern Web Application Firewalls.
Security Methodology: Trained on AYI-NEDJIMI/bug-bounty-pentest-en to master the logical structure of pentesting logs and methodology.
Real-World Experience: Augmented with HackerOne Disclosed Reports (scraped from Hacktivity) and curated GitHub Writeups (2025-2026) to learn successful exploit chains.
Architectural Foundation: Follows the implementation principles of Sebastian Raschka's "LLMs from scratch".

Technical Specs

Base Model: Qwen2.5-Coder-7B-Instruct
Fine-Tuning: QLoRA (Rank 64, Alpha 64)
Context Window: 4096 Tokens
Precision: bfloat16 (Optimized for NVIDIA Ampere architecture)

🛠️ Usage (BugTraceAI-CLI Integration)

BugTraceAI-CORE is designed to work as a plug-and-play replacement for external APIs.

# Example environment configuration
export OPENROUTER_BASE_URL="http://your-local-core:8000/v1"
export OPENROUTER_API_KEY="sk-bugtrace-local-core"

System Architecture

Port 8000: Gateway (FastAPI) - Intelligent router that directs traffic.
Port 8001: GPU Node (vLLM) - High-speed primary inference.
Port 8002: CPU Node (Llama.cpp) - Reliable fallback for the Dual Xeon.

⚠️ Disclaimer

BugTraceAI-CORE is intended for legal ethical hacking and educational purposes only. The creators are not responsible for any misuse of this tool. Always ensure you have explicit permission before testing any system.

Created as part of the BugTraceAI Ecosystem. Building a more secure web, one report at a time.