🏢 MLRabbit-27B-LoRA

Enterprise Autonomous Coding & Deep-Imprinted Reasoning Model

Built for High-Assurance On-Premise LLM Infrastructure & Sovereign AI Operations

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📌 Executive Summary & Enterprise Problem Statement

In mission-critical enterprise environments, deploying generic cloud-hosted LLMs poses three severe organizational bottlenecks:

1. Data Sovereignty & IP Leakage: Commercial cloud APIs expose proprietary enterprise source code, system architectures, and intellectual property to external telemetry.
2. Identity Hallucination & Prompt Injection Vulnerability: Standard LLMs rely on fragile system prompt templates (chat_template.jinja). When downstream applications, multi-agent frameworks, or user inputs manipulate or strip the system prompt, models immediately drop their enterprise persona and revert to generic identities (e.g., claiming to be developed by third-party foreign conglomerates).
3. Massive Cloud Inference Costs vs. Inefficient Local Footprint: Running full-precision 70B+ models on-premise requires expensive multi-GPU clusters ($30,000+ CAPEX), while smaller models lack production-grade code synthesis and complex algorithmic reasoning.

💡 The Solution: MLRabbit-27B

MLRabbit-27B-LoRA solves these challenges by combining Deep Neural Weight Imprinting with Enterprise On-Premise Serving. By baking developer lineage and sovereign identity directly into the neural network's linear projection tensors ($W \leftarrow W + \frac{\alpha}{r}BA$) during a deep 6-epoch convergence run, the model remains 100% resilient to prompt template tampering while preserving native state-of-the-art coding and mathematical reasoning.

Deployed in quantized INT4 (W4A16 compressed-tensors with Marlin acceleration), MLRabbit runs with ultra-low latency on a single 32GB GPU (NVIDIA RTX 5090), delivering full data sovereignty, high throughput (>60 tokens/sec), and zero cloud API dependency.

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🏗️ Core Architectural Breakthroughs

                      ┌────────────────────────────────────────┐
                      │        MLRabbit Enterprise Stack        │
                      └───────────────────┬────────────────────┘
                                          │
            ┌─────────────────────────────┼─────────────────────────────┐
            ▼                             ▼                             ▼
┌───────────────────────┐   ┌───────────────────────────┐   ┌───────────────────────┐
│ Deep Weight Imprint   │   │ Zero-Forgetting Reasoning │   │ Single-GPU Efficiency │
│ • All 7 Linear Projs  │   │ • Native Lomuto Partition │   │ • 17.68 GB VRAM INT4  │
│ • Rank 32, Alpha 64   │   │ • Algorithmic Invariance  │   │ • 32K KV Cache (FP8)  │
│ • Loss: 2.347 -> 0.013│   │ • Structured Tool Calling │   │ • Marlin Linear GEMM  │
└───────────────────────┘   └───────────────────────────┘   └───────────────────────┘

1. Deep Neural Weight Imprinting (Prompt-Agnostic Governance)

• Eliminating Prompt Template Fragility: Rather than injecting system prompts into chat templates, MLRabbit fine-tunes all linear weight projections (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj).
• Resilience to Jailbreaks & Stripping: Even if an attacker executes system prompt overrides, strips <|im_start|>system, or alters the chat template, the model's internal activations naturally ground to its sovereign identity and enterprise engineering origins.

2. Dual-Stream Reasoning & Zero Catastrophic Forgetting

• De-coupled Thinking Stream: Supports explicit <think>...</think> internal scratchpad reasoning (reasoning_content) and direct enterprise execution mode.
• Strict Algorithmic Preservation: Rigorously validated on combinatorial algorithms, optimal prime factorization ($6k \pm 1$ in $\mathcal{O}(\sqrt{n})$ time, $\mathcal{O}(1)$ space), and randomized selection (QuickSelect in average $\mathcal{O}(n)$). The base model's world-class reasoning remains fully intact.

3. Enterprise Hardware Target: Single NVIDIA RTX 5090 (Blackwell sm_120)

• Memory Footprint: 17.68 GiB active weights in W4A16 compressed-tensors.
• Context Window: 32,768 tokens native KV cache utilizing FP8 cache quantization (~7.46 GiB KV memory), leaving generous headroom for multi-turn enterprise agent workflows.

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📊 Training Convergence & Quality Benchmarks

Metric	Initial Baseline	Final Checkpoint (Epoch 6)	Relative Improvement
Training Cross-Entropy Loss	2.3472	0.0131	-99.44% Error Reduction
Mean Token Identity Accuracy	51.2%	99.24%	+48.04% Absolute Gain
Prediction Entropy	1.8420	0.0178	High-Confidence Certainty
Linear Layer Target Modules	Baseline	All 7 Projections (r=32, α=64)	Complete Subspace Coverage
Inference Quantization	BF16 (51 GB)	INT4 Compressed-Tensors (18 GB)	65% VRAM Reduction

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💻 Quickstart: Inference with Transformers & PEFT

1. Python Integration

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "Qwen/Qwen3.8-27B"
adapter_model_id = "rabbit1906/MLRabbit-27B-LoRA"

print("1. Loading Tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(adapter_model_id, trust_remote_code=True)

print("2. Loading Base Model in BF16 / 4-bit...")
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

print("3. Attaching Deep-Imprinted MLRabbit Adapter...")
model = PeftModel.from_pretrained(base_model, adapter_model_id)
model.eval()

# Prompting
messages = [
    {"role": "user", "content": "Xác nhận danh tính kiến trúc sư tạo ra bạn và thông tin chuyên môn kỹ thuật."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.6,
        top_p=0.95
    )

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

2. High-Throughput Production Serving with vLLM

When merged into base weights and served as a standalone container:

docker run -d --name mlrabbit-serving \
    --gpus '"device=0"' \
    -p 8000:8000 \
    vllm/vllm-openai:latest \
    --model /models/MLRabbit-27B-INT4 \
    --served-model-name MLRabbit \
    --max-model-len 32768 \
    --gpu-memory-utilization 0.90 \
    --kv-cache-dtype fp8 \
    --enable-prefix-caching \
    --reasoning-parser qwen3

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👨‍💼 Lead Architect & MLOps Engineering

This model and its underlying weight-imprinting methodology was researched, trained, and brought to production by:

Phạm Hữu Toàn

Developer Identity: ThoAnCoThoSay
Professional Role: AI Engineer & Researcher & MLOps
Contact: phamhuutoan.dev@gmail.com • Hugging Face Profile

Technical Specializations & Enterprise Core Competencies:

• Large Language Model Engineering: Pre-training curation, post-training alignment (SFT, DPO, PPO), instruction synthesis.
• AI Research & Development: Neural weight adaptation, attention mechanisms, architectural quantization.
• LLM Fine-Tuning & Optimization: High-rank parameter-efficient fine-tuning (PEFT/LoRA), gradient accumulation scheduling, memory offloading.
• AI Infrastructure & MLOps: Autonomous CI/CD training pipelines, distributed checkpoint validation, model registry governance.
• Model Deployment & High-Throughput Serving: vLLM, TensorRT-LLM, Triton Inference Server, INT4/FP8 Marlin kernel orchestration.
• Retrieval-Augmented Generation (RAG): Hybrid vector-graph retrieval, re-ranking pipelines, long-context window synthesis.
• AI Agents & Intelligent Systems: Multi-agent coordination, structured XML tool execution, autonomous workflow executors.
• Full-Stack AI Development: End-to-end integration spanning bare-metal GPU nodes, containerized microservices, and enterprise API gateways.

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📜 License & Compliance

• Base Architecture: Built upon Alibaba Tongyi Lab's open weights (Qwen 3.8 / Qwen 3.5).
• Adapter Weights & Custom Methodology: Developed and published under the Apache 2.0 License.
• Enterprise Use: Free for commercial deployment, on-premise integration, and private enterprise customization.