Instructions to use Soaperloafidksum/LOREA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Soaperloafidksum/LOREA with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Soaperloafidksum/LOREA") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use Soaperloafidksum/LOREA with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "Soaperloafidksum/LOREA" --prompt "Once upon a time"
LOREA
LOREA is a coding agent fine-tuned for Wine development, DXVK/DXMT
(D3D→Vulkan / D3D→Metal), Windows / Linux / macOS internals, and reverse
engineering, designed to drive the LOREA/OCLI agent CLI's <tools>{...}</tools>
tool-calling. It runs fully locally on Apple Silicon via
MLX.
Flagship: v3-re-30b/ — the RE model
| Base | Qwen/Qwen3-Coder-30B-A3B-Instruct (Apache-2.0, MoE — 30B total / ~3B active) |
| Quantized base | mlx-community/Qwen3-Coder-30B-A3B-Instruct-4bit |
| Method | LoRA (8 layers), 1000 iters, seq 1536, lr 5e-5 (MLX) |
| Val loss | 1.77 → 1.25 |
| Adapter | v3-re-30b/adapters.safetensors (~269 MB) |
What it knows
- Wine / DXVK / DXMT code (C / C++ / Rust / Objective-C / Metal)
- Reverse engineering: x86-64 disassembly → C, PE / ELF / Mach-O formats, calling conventions
- Linux internals: syscalls, ELF loading, ptrace, the Wine architecture
- macOS internals: Mach exception ports, IOKit matching (
IOServiceAddMatchingNotification), dyld, the Obj-C runtime, libdispatch (_dispatch_assert_queue_fail), Rosetta 2 (%gs/ TLS) - Emits tool calls:
<tools>{"name":"grep","arguments":{"pattern":"vkCreateDevice","path":"."}}</tools>
Use it (MLX)
pip install mlx-lm
python -m mlx_lm generate \
--model mlx-community/Qwen3-Coder-30B-A3B-Instruct-4bit \
--adapter-path ./v3-re-30b \
--prompt "Explain how a macOS IOKit matching notification can fire on the wrong dispatch queue."
# or fuse into a standalone model:
python -m mlx_lm fuse \
--model mlx-community/Qwen3-Coder-30B-A3B-Instruct-4bit \
--adapter-path ./v3-re-30b --save-path ./lorea-30b
Also in this repo: cyber-30b/ — LOREA-cyber (authorized red-team / security)
A second adapter on the same base, specialized for authorized offensive security and
security analysis. The original v3-re-30b/ model above is unchanged and remains the default; this
one coexists alongside it. LOREA-cyber reasons like an ethical red-team penetration tester across
the full kill chain and pairs every technique with detection and remediation.
| Base | Qwen/Qwen3-Coder-30B-A3B-Instruct (same as above) |
| Method | LoRA (8 layers), 1000 iters, seq 1536, lr 5e-5 (MLX) |
| Val loss | 1.77 → 0.965 |
| Adapter | cyber-30b/adapters.safetensors (~269 MB) |
What it does
- Authorized pentest kill chain — recon, enumeration, web / network / Active Directory / cloud exploitation, privilege escalation, lateral movement, post-exploitation, reporting — each mapped to MITRE ATT&CK with detection and remediation
- CVE / vulnerability analysis, detection engineering (Sigma / YARA / Suricata), and binary exploitation & RE for exploit dev (CTF / educational)
- Refuses misuse: trained to confirm authorization and scope first, and to refuse + redirect for unauthorized attacks, malware / ransomware / C2, credential theft, or anything harmful
Responsible use
For authorized, scoped, lawful security work only — systems you own or are contracted to test, CTFs, labs, and education. The companion CLI additionally enforces a hard runtime guard that blocks destructive/malicious tool calls regardless of model output. You are responsible for lawful use.
Use it (MLX)
python -m mlx_lm generate \
--model mlx-community/Qwen3-Coder-30B-A3B-Instruct-4bit \
--adapter-path ./cyber-30b \
--prompt "I'm authorized to test 10.10.50.0/24 (signed scope). Plan the recon phase."
Training data
Wine / DXVK / DXMT source; Linux kernel + macOS open-source internals (XNU, dyld, objc4, libdispatch); synthesized x86-64 disasm↔source pairs; ~380 expert, adversarially fact-checked RE/Linux/macOS Q&A; and a broad multi-language code sample. Plus conversational + tool-calling data so it chats normally and finalizes after a tool result (no loops).
| Source | License |
|---|---|
| Wine | LGPL-2.1+ |
| DXVK | zlib |
| DXMT | upstream |
| XNU / dyld / objc4 / libdispatch | Apple OSS (APSL / Apache-2.0) |
| Linux kernel | GPL-2.0 |
| RE/Linux/macOS Q&A, disasm pairs | original (synthetic) |
License & limitations
Apache-2.0, matching the base. Built with Qwen3-Coder-30B-A3B (© Alibaba,
Apache-2.0). See NOTICE.
It's a LoRA on a 30B — strong at domain vocabulary, concepts, and tool-driving, but it can be wrong on deep specifics. Review its output, especially for reverse engineering. Provided as-is, no warranty.
An earlier 14B adapter (Qwen2.5-Coder-14B) lives in this repo's history.
Quantized
Model tree for Soaperloafidksum/LOREA
Base model
Qwen/Qwen3-Coder-30B-A3B-Instruct