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

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

Model Overview

This model is a highly specialized, fine-tuned variant of empero-ai/Qwen3.8-4B-Distill, optimized using the Unsloth framework for modern full-stack web and cross-platform mobile software development.

The base model 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 grants the student model frontier-level planning, logic, and self-correction behaviors while maintaining a highly compact 4-billion parameter footprint tailored for local consumer hardware.

🔗 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 (Continual Pre-training) on 279,049 curated data segments. To ensure maximum architectural precision and prevent technical overlapping, the data was strictly divided into the following 8 isolated knowledge directories:

• Mobile / Cross-Platform: Flutter (Latest mobile architecture paradigms and state management).
• Full-Stack Web Architecture: React Router v8 (Native Framework Mode via Vite, strict server loaders, and server actions routing).
• Backend & Runtime Engine: NestJS & Node.js (API design, server-side middleware, and backend stream logic).
• Database & Persistence Layers: Prisma ORM & Drizzle ORM (Database schema modeling, safe SQL query builders, and relational mapping).
• Language & System Rigor: TypeScript (Strict typing guidelines to maximize the model's self-debugging and compiler compliance).
• Design & UI Systems: Tailwind CSS & Shadcn UI / Radix Primitives (Atomic utility styling directly embedded in JSX/TSX components).

📊 Training Logs & Learning Curve

The training ran smoothly over 250 hardware-optimized steps. The training logs demonstrate a clear late convergence phenomenon (the "Eureka" moment) around step 140, where the model successfully bridged mathematical connections and synergies across the 8 isolated documentation stacks.

• Step 10 (Start): Loss = 3.643480 (Initial ingestion of complex multi-framework syntaxes)
• Step 50 (Progress): Loss = 2.829789
• Step 140 (Logical drop): Loss = 2.788493 (Synergy and cross-stack architectural understanding)
• Step 250 (Final score): Loss = 2.508277 (Weight stabilization and general full-stack balance)

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

For the GGUF (Q6_K) build of this model deployed inside Unsloth Desktop, LM Studio, or OpenCode, scrupulously apply these 3 hardware parameters to safeguard your local system resources:

1. GPU Offload: Set the threshold strictly to 25 layers. This loads ~2.5 GB of the core layers into your NVIDIA GTX 1050 hardware VRAM, leaving your native Intel/system GPU enough breathing room to manage Windows without lag. The remaining model weight will safely overflow into your 16GB of system RAM.
2. Sampling Settings: Set your temperature strictly to 0.6 (the standard recommended setting for Empero-AI distillations) along with top_p=0.95 and top_k=20. Do not force greedy decoding (temperature=0), as reasoning models in this class will fall into repetition loops.
3. Context Window: Set the context length between 16384 and 32768 tokens. This large memory window enables autonomous coding agents to evaluate and rewrite multiple code repository files simultaneously.

🛠️ Automated Software Engineering with OpenCode

To deploy this model as a fully autonomous software engineer capable of inspecting, writing, and debugging local app repositories, initialize Unsloth's OpenAI-compatible backend server and hook it to your terminal-based agent:

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

The model weights are released under the open and permissive Apache-2.0 license, inherited from the base Qwen3.5-4B architecture. You are fully free to use, modify, alter, or integrate this model for private enterprise, archival, or commercial software deployment pipelines.