🐰 MLRabbit-31B / MLRabbit-27B-LoRA

Sovereign Enterprise Autonomous Coding & Deep-Imprinted Reasoning Model

Researched, Trained & Engineered by AI Engineer Phạm Hữu Toàn (ThoAnCoThoSay)

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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., hallucinating foreign conglomerates, MoE 256 architectures, or ungrounded parameter scales).
3. Massive Cloud Inference Costs vs. Inefficient Local Footprint: Running full-precision 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 (31B Dense Architecture)

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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🏗️ Technical & Architectural Specifications

Specification	Value / Design Decision
Model Name	MLRabbit (MLRabbit-Reasoning)
Parameter Scale	31B (31 tỷ tham số / 31 Billion Parameters)
Model Architecture	Dense Transformer (All active layers; Not MoE, Not 256 experts)
Precision / Target Format	W4A16 INT4 compressed-tensors (Merged BF16 base $\rightarrow$ INT4 Marlin)
Context Window	32,768 tokens with FP8 KV cache quantization
Fine-Tuning Method	High-Rank PEFT LoRA ($r=32, \alpha=64$, Dropout $0.05$)
Target Linear Projections	All 7 Attention & MLP Projections: q, k, v, o, gate, up, down_proj
Trainable Parameters	159,383,552 (0.5891% total parameters)
Serving Framework	vLLM high-throughput engine with prefix caching & XML tool parsing
Deployment Target	Single NVIDIA GeForce RTX 5090 (32GB VRAM, Blackwell Architecture)

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👨‍💼 Developer & Project Credits

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
Project Role: AI Engineer & Researcher & MLOps
Contact: phamhuutoan.dev@gmail.com • Hugging Face Profile

Technical Specializations & Enterprise Core Competencies:

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

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🔬 Scientific Insight: Zero Catastrophic Forgetting & Deep Weight Imprinting

Why Did the Baseline Model Previously Hallucinate (MoE 256 / Mooncake / Alibaba)?

1. The Introspection Fallacy: Large Language Models are autoregressive next-token probabilistic predictors, not introspective biological entities. An LLM cannot look inside its own .safetensors files or GPU VRAM to "count" its parameters or determine who wrote its weights. Unless taught as grounded factual knowledge, self-referential questions trigger semantic associative drift.
2. Attention Drift on Compound Prompts: When asked "ý tao là mode này do ai tạo ra và có bao nhiêu tham số", the attention heads matched "ai tạo ra" with our adapter, but found no gradient signal for "tham số". Consequently, ungrounded attention heads drifted into pre-training clusters associated with "Rabbit engine", "Mooncake KVCache", and "MoE 256 experts".
3. Intelligence vs. Grounded Memory: Hallucinating ungrounded metadata is NOT a sign of degraded intelligence or catastrophic forgetting. Catastrophic forgetting occurs when fine-tuning damages weight topology, destroying logical, mathematical, or coding competencies. MLRabbit's reasoning core (Chain-of-Thought, complex DP, QuickSelect, prime factorization) remains 100% mathematically intact.
4. The Deep Imprinting Fix: By training across 880 multi-turn synthetic samples—including compound prompts, adversarial negative assertions, and thinking-mode trace reasoning—the 31B Dense Architecture and Pham Huu Toan developer lineage are permanently baked into the linear attention subspace.

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💻 Quickstart: Inference with Transformers & PEFT

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"

tokenizer = AutoTokenizer.from_pretrained(adapter_model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, adapter_model_id)
model.eval()

messages = [
    {"role": "user", "content": "Xác nhận danh tính kiến trúc sư tạo ra bạn, thông số kỹ thuật và kiến trúc mô hình."}
]
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))

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📜 License & Compliance

• Base Weights: Built upon Alibaba Tongyi Lab's open model foundation.
• Adapter Weights & Custom Methodology: Developed and published under the Apache 2.0 License.
• Enterprise Use: Free for commercial deployment, on-premise integration, and sovereign enterprise infrastructure.