Qwen3.8-4B-Empero-AI-FullStack-GGUF

Qwen3.8 4B Distill by Empero-AI and finetuned by iWebRoot — GGUF

Model Overview

This repository contains the optimized GGUF quantization of Qwen3.8-4B-Empero-AI-Distill-FullStack, fine-tuned using the Unsloth framework for advanced full-stack web and mobile software development pipelines.

The base architecture features a full-parameter distillation of reasoning traces (Chain-of-Thought via <think>...</think> tags) from the frontier-scale Qwen3.8 2.4T A95B teacher model developed by Empero-AI. This configuration offers advanced local planning, logic, and code compilation compliance within a highly efficient 4-billion parameter footprint.

🔗 Repository Links

• Safetensors Version (9.3GB Heavy Build): https://huggingface.co/iBotIA/Qwen3.8-4B-Empero-AI-FullStack
• GGUF Version (3.5GB Optimized Quantization): https://huggingface.co/iBotIA/Qwen3.8-4B-Empero-AI-FullStack-GGUF

📚 Injected Knowledge Stack (Fine-Tuning Data)

The model underwent continuous pre-training on 279,049 curated data segments across 8 strictly isolated developer knowledge directories:

• Mobile / Cross-Platform: Flutter (Modern structural widgets and lifecycle state management).
• Full-Stack Web Architecture: React Router v8 (Framework Mode via Vite, server-loaders, and async server-actions routing).
• Backend & Runtime Engine: NestJS & Node.js (API architecture, scalable middleware, and server streams).
• Database & Persistence Layers: Prisma ORM & Drizzle ORM (Schema modeling, relational builders, and safe SQL migrations).
• Language & System Rigor: TypeScript (Strict typing patterns to enforce self-debugging and runtime stability).
• Design & UI Systems: Tailwind CSS & Shadcn UI / Radix Primitives (Utility class layout embedded in JSX/TSX components).

📊 Training Logs & Learning Curve

The fine-tuning process completed 250 hardware-optimized steps on a T4 GPU. The learning curve showed a definitive late convergence ("Eureka" moment) near step 140, where the weights successfully aligned cross-stack framework logic.

• Step 10 (Start): Loss = 3.643480
• Step 50: Loss = 2.829789
• Step 140 (Logical drop): Loss = 2.788493
• Step 250 (Final score): Loss = 2.508277

📦 Quantization Specifications

• File: Qwen3.8-4B-Empero-AI-Distill-FullStack-Q6_K.gguf
• Format: Q6_K (6-bit quantization)
• Size: ~3.56 GB
• Quality: Near-lossless precision compared to the 16-bit reference build.

💻 Local Execution Guide (Target: GTX 1050 4GB VRAM)

When deploying this GGUF file inside Unsloth Desktop, LM Studio, Jan, or Ollama, configure these 3 runtime settings to prevent system stuttering:

1. GPU Offload: Set your hardware layer slider to 25 layers. This safely loads ~2.5 GB of the model weight into your NVIDIA GTX 1050 VRAM without freezing Windows, while the remaining compute safely overflows into your 16GB system RAM.
2. Sampling Settings: Set temperature=0.6, top_p=0.95, and top_k=20. Avoid a raw greedy search (temperature=0) to prevent the reasoning tokens from falling into endless structural loops.
3. Context Window: Set the token length to 16384 or 32768. This expanded context window allows autonomous agents to evaluate several source files at the same time.

🛠️ Execution with OpenCode Autonomous Agent

To launch this model as an active developer backend connected to your terminal agent, run the OpenAI-compatible local engine server:

unsloth start opencode --context-length 32000

Support / Donate

If this model helped you, consider supporting the project:

• BTC: 18cBC5sFjtctw121ULTkxTbTZPurginJBs
• LTC: ltc1q3jrcwrx66xpz4k92p08u8c5v8zwywk3dqpzdkv
• USDT: TGKVpbbznmvEusKbuZZj4WSK6XxtHcG6FE (TRX chain)
• USDT: 0x1059cb5a1F8467e5b56a9bdf082cE86FFB002D15 (POL chain)
• USDT: 0x18b2AA731daeFD47DFFa278f3F856eAF80376fd6 (ETH chain)
• USDT: 0x3bEcddC7c49bDba5503eB1677628b4519439884c (BNB chain)

Provenance & Licensing

Quantizations are built upon empero-ai/Qwen3.8-4B-Distill. Weights inherit the permissive Apache-2.0 license from the base Qwen repository and are shared as-is.