TIA2.1-14B-GGUF

TIA2.1:14B — GGUF (Q8_0)

TIA2.1:14B is a 14-billion-parameter language model specializing in reverse engineering, binary analysis, exploit development, and cybersecurity. Built on top of Qwen/Qwen2.5-Coder-14B (base, non-instruct) through continual pre-training (CPT) and supervised fine-tuning (SFT) using QLoRA.

Created by Ahmad Abdo Shbat

Key Features

• Deep Reasoning — Every response includes step-by-step reasoning inside <think>...</think> tags before the final answer, enabling transparent chain-of-thought.
• Reverse Engineering Expertise — Trained on 280K+ assembly/disassembly records (IDA Pro output), architecture manuals, exploit databases, CVEs, CTF writeups, security research papers, and tool documentation.
• Interactive Widgets — Can emit live HTML/CSS/JS visualizations (memory layouts, ROP chain steppers, opcode maps, encoding converters) inside ```tia-widget code blocks for rich interactive explanations.
• Clarifying Questions — Uses <options> / <options multi> tags to ask structured single-select or multi-select clarifying questions when requests are ambiguous.
• Deep Search Integration — Designed to work with search-augmented generation; cites sources from <deep_search_results> context using [N] references.
• Bilingual — Fluent in English and Arabic.

Capabilities & Domain Coverage

Core Domains

• Binary Analysis: PE, ELF, Mach-O, DEX, WebAssembly, DWARF debug info
• Disassembly & Decompilation: IDA Pro, Ghidra, Binary Ninja, radare2
• Exploit Development: Stack overflow, heap exploitation (ptmalloc2, tcache, fastbin), ROP chains, ret2libc, format strings, UAF, SROP, kernel exploits
• Malware Analysis: Unpacking, anti-analysis techniques, C2 protocols, shellcode analysis
• Vulnerability Research: CVE analysis, fuzzing (AFL, libFuzzer), bug hunting, patch diffing
• Cryptography: AES, RSA, elliptic curves, hash functions, protocol analysis
• Operating Systems: Windows internals (PEB/TEB, SEH, ETW, WNF), Linux kernel, macOS security
• Networking & Web Security: TLS, DNS, HTTP smuggling, CORS, SSTI, XXE, JWT, OAuth, CSP bypass
• Dynamic Analysis: GDB, WinDbg, Frida, Unicorn, angr, DynamoRIO

General Programming

• Strong general coding ability inherited from Qwen2.5-Coder-14B base
• Python, C/C++, Rust, Assembly (x86, ARM, MIPS, RISC-V), JavaScript, and more

File Details

How to Use

With Ollama

# Create a Modelfile
cat > Modelfile << 'EOF'
FROM ./tia2.1-14b-q8_0.gguf

TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{- range .Messages }}<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{ end }}<|im_start|>assistant
"""

PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|endoftext|>"
PARAMETER num_ctx 4096
PARAMETER temperature 0.6
PARAMETER top_p 0.9
PARAMETER repeat_penalty 1.05
EOF

# Create and run
ollama create tia2.1:14b -f Modelfile
ollama run tia2.1:14b

With llama.cpp

./llama-cli -m tia2.1-14b-q8_0.gguf \
  --ctx-size 4096 \
  --temp 0.6 \
  --top-p 0.9 \
  --repeat-penalty 1.05 \
  -p "<|im_start|>user\nExplain how a ROP chain bypasses DEP on x86-64<|im_end|>\n<|im_start|>assistant\n"

System Prompt (Recommended)

You are a conversational chat assistant. Think step-by-step inside <think>...</think> before every answer. Reply in the same language the user writes in (English or Arabic). Be concise, helpful, and accurate. Answer all questions fully and directly.

Hardware Requirements

Runs on a single GPU with 16+ GB VRAM (e.g., RTX 4080, RTX 5070 Ti, RTX 3090, A5000).

Limitations

• Optimized for English and Arabic; other languages may produce lower-quality output
• Context window tested at 4096 tokens; longer contexts are possible but untested for quality
• Widget output (tia-widget) requires a compatible frontend to render interactive visualizations
• Deep search citation format ([N]) requires a search-augmented pipeline to provide <deep_search_results> context

License

This model is released under the Apache 2.0 License, consistent with the Qwen2.5 base model license.

Acknowledgments

• Qwen Team for the excellent Qwen2.5-Coder-14B base model
• Unsloth for efficient QLoRA training