---

license: apache-2.0

tags:

• gguf
• text-generation
• qwen
• web-development
• accessibility
• software-architecture
• frontend-performance

base_model: Qwen/Qwen3-4B-Thinking-2507

model_creator: callmechaubey

model_name: Qwen3-4B-Plus

pipeline_tag: text-generation

quantized_by: callmechaubey

---

> [!NOTE]

> ## 💬 Connect with the Creator

>

> Have questions, feedback, ideas, or just want to chat about AI, Linux, open source, or software development?

>

> 📸 Instagram: https://instagram.com/call.me_chaubey

>

> Feel free to reach out—I'm always happy to connect with fellow developers, AI enthusiasts, and anyone interested in technology.

Qwen3-4B-Plus (Quantized GGUF)

Qwen3-4B-Plus is a fine-tuned version of Qwen/Qwen3-4B-Thinking-2507, optimized for Elite Web Development Consultations, Software Architecture, Frontend Performance Engineering, Accessibility (a11y) Audits, and Production Refactoring.

This repository contains the model quantized to the highly efficient Q4_K_M GGUF format, making it ideal for local inference on laptops and consumer-grade hardware via LM Studio, llama.cpp, or Ollama.

---

🔬 Fine-Tuning Analysis & Model Differences

The model was fine-tuned using a high-quality dataset of 506 curated web engineering scenarios (webdev_elite_finetuning_dataset.jsonl). Below is an in-depth analysis of how this fine-tuning transforms the model from a generic assistant into an elite web development consultant:

Comparison: Base Qwen3 vs. Qwen3-4B-Plus

| Feature / Topic | Base Model (Qwen3-4B-Thinking-2507) | Fine-Tuned Model (Qwen3-4B-Plus) |

| :--- | :--- | :--- |

| Framework Recommendations | Suggests frameworks (React, Vue, Next.js) with generic, conversational descriptions. | Recommends specific, modern versions (e.g., Next.js 15, Astro, Remix) mapped strictly to workloads (SaaS, SEO portals, form-heavy apps) with structured trade-offs (caching, hydration, dev experience). |

| Architecture (ADRs) | Recommends complex microservices early; answers lack structured consequences. | Follows strict Architecture Decision Record (ADR) formats. Defaults to modular monoliths first; outlines specific trade-offs, scalability milestones, and consequences. |

| Accessibility (a11y) | Mentions basic accessibility practices like "use alt text" or "use semantic tags." | Enforces rigorous audits covering keyboard navigation, focus trapping, label associations, ARIA roles, screen-reader announcements, and reduced-motion media. |

| Performance Auditing | Gives general tips like "minify code" and "use a CDN." | Focuses on modern Core Web Vitals (LCP, INP, CLS), bundle size reduction, code splitting, lazy loading, hydration cost mitigation, and validation methods. |

| Consultation & Dialogue Flow | Attempts to solve everything at once, leading to overwhelmed responses. | Follows an incremental consultation flow: starts with the core feature MVP, then progresses logically through architecture, security, observability, testing, and deployment. |

Core Dataset Focus Areas

The 506 training rows are divided into structured developer tasks:

1. Framework Selection & Evaluation: Objective rubrics matching architectures to business needs.
2. Architecture Decision Records (ADRs): Real-world growth scenarios, preventing over-engineering.
3. Accessibility Audits: Detailed compliance testing protocols for interactive web components.
4. Performance Reviews: Core Web Vitals diagnostics and modern frontend loading optimizations.
5. Production Refactoring: Methods for refactoring legacy modules to TypeScript interfaces and decoupled patterns.
6. Consultation Flow Control: Keeping agent interactions structured, professional, and progressive.

---

📂 File Contents & Quantization Specs

• Quantization Type: Q4_K_M (4-bit quantization using medium-sized K-quants).
• Format: GGUF (.gguf).
• Original Model Size: ~8.0 GB (Float16).
• Quantized Model Size: ~2.33 GB (2375.91 MiB).
• Inference Performance (AMD Ryzen 5 8645HS CPU):

- Prompt Evaluation Speed: 30.9 t/s

- Token Generation Speed: 28.1 t/s

---

💻 How to use in LM Studio

1. Open LM Studio.
2. Click on the Folder Icon (My Models) on the left sidebar.
3. Click "Show in Explorer" to open your local LM Studio models directory in Windows Explorer.
4. Navigate or create a publisher directory structure inside the models folder:

```

C:\Users\<your-username>\.cache\lm-studio\models\callmechaubey\Qwen3-4B-Plus-GGUF\Q4_K_M\

```

1. Move the Qwen3-4B-Plus-Q4_K_M.gguf file into that folder.
2. Return to LM Studio, select the model from the dropdown at the top, and start chatting!

---

🛠️ How to run in llama.cpp CLI

You can execute local inference directly from your terminal using:

llama-cli.exe -m Qwen3-4B-Plus-Q4_K_M.gguf -p "What is the best rendering strategy for a multi-tenant SaaS dashboard?" -n 256