---
license: apache-2.0
base_model: empero-ai/Qwen3.8-4B-Distill
tags:
- text-generation
- qwen3_5
- distillation
- reasoning
- full-stack
- typescript
- react-router-v8
- nestjs
- flutter
- unsloth
inference: false
model_type: qwen3_5
---

# 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](https://huggingface.co/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](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](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)](https://huggingface.co/iBotIA/Qwen3.8-4B-Empero-AI-FullStack-GGUF)** 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:

```bash
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.