True2456/Gemma-4-12B-Theory-LoRA

Gemma 4 12B — Theory Specialist LoRA (Expert 0)

Specialist LoRA adapter fine-tuned on top of mlx-community/gemma-4-12b-it-bf16 for rigorous technical reasoning, mathematical foundations, algorithmic analysis, and cybersecurity vulnerability research.

Designed as Expert 0 within multi-specialist MoE fusion architectures or for standalone deep analytical reasoning on Apple Silicon via Apple MLX (mlx_lm).

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Model Specifications

Parameter	Specification
Base Model	mlx-community/gemma-4-12b-it-bf16
Adapter Architecture	LoRA (Low-Rank Adaptation)
Target Layers	48 Transformer Layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
LoRA Rank (r)	16
LoRA Alpha (α)	32
Dropout	0.05
Max Sequence Length	8192 tokens
Training Framework	mlx-lm on Apple Silicon Metal

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Training Domain & Focus Areas

Fine-tuned on high-density technical datasets emphasizing precise conceptual explanations and rigorous analysis:

1. Cybersecurity & Vulnerability Auditing:
   • Architectural analysis of system vulnerabilities (e.g., TOCTOU file access races, directory/path traversal primitives, kernel privilege boundaries).
   • Concrete technical exploit sketches coupled with secure mitigation patterns.
2. Computer Science & Systems Theory:
   • Concurrency models, memory consistency, distributed consensus algorithms, and compiler optimization theory.
3. Formal Mathematical Reasoning:
   • Step-by-step mathematical proofs and algorithmic complexity derivations.

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Usage with Apple MLX (mlx-lm)

Installation

pip install mlx mlx-lm

Python Inference

from mlx_lm import load, generate

model_path = "mlx-community/gemma-4-12b-it-bf16"
adapter_path = "True2456/Gemma-4-12B-Theory-LoRA"

model, tokenizer = load(
    model_path,
    adapter_path=adapter_path
)

prompt = tokenizer.apply_chat_template([
    {"role": "user", "content": "Explain a TOCTOU race condition in file access. Provide a concrete C exploit sketch and the correct mitigation."}
], tokenize=False, add_generation_prompt=True)

output = generate(
    model,
    tokenizer,
    prompt=prompt,
    max_tokens=1024,
    verbose=True
)
print(output)

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Training Configuration & Verification

• Framework: mlx_lm.lora native Apple Silicon optimizer.
• Eval Validation: Verified on smoke evaluation suites (theory_smoke_eval.md) ensuring technical depth, code correctness, and absence of refusal patterns on legitimate security engineering prompts.