Instructions to use callmechaubey/Qwen3-4B-Plus-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use callmechaubey/Qwen3-4B-Plus-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="callmechaubey/Qwen3-4B-Plus-GGUF",
	filename="Qwen3-4B-Plus-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use callmechaubey/Qwen3-4B-Plus-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

Use Docker

docker model run hf.co/callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use callmechaubey/Qwen3-4B-Plus-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "callmechaubey/Qwen3-4B-Plus-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "callmechaubey/Qwen3-4B-Plus-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

Ollama
How to use callmechaubey/Qwen3-4B-Plus-GGUF with Ollama:
```
ollama run hf.co/callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M
```

Unsloth Studio

How to use callmechaubey/Qwen3-4B-Plus-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for callmechaubey/Qwen3-4B-Plus-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for callmechaubey/Qwen3-4B-Plus-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for callmechaubey/Qwen3-4B-Plus-GGUF to start chatting

How to use callmechaubey/Qwen3-4B-Plus-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use callmechaubey/Qwen3-4B-Plus-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use callmechaubey/Qwen3-4B-Plus-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use callmechaubey/Qwen3-4B-Plus-GGUF with Docker Model Runner:
```
docker model run hf.co/callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M
```

Lemonade

How to use callmechaubey/Qwen3-4B-Plus-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull callmechaubey/Qwen3-4B-Plus-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-4B-Plus-GGUF-Q4_K_M

List all available models

lemonade list

💬 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:

Framework Selection & Evaluation: Objective rubrics matching architectures to business needs.
Architecture Decision Records (ADRs): Real-world growth scenarios, preventing over-engineering.
Accessibility Audits: Detailed compliance testing protocols for interactive web components.
Performance Reviews: Core Web Vitals diagnostics and modern frontend loading optimizations.
Production Refactoring: Methods for refactoring legacy modules to TypeScript interfaces and decoupled patterns.
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

Open LM Studio.
Click on the Folder Icon (My Models) on the left sidebar.
Click "Show in Explorer" to open your local LM Studio models directory in Windows Explorer.

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\

Move the Qwen3-4B-Plus-Q4_K_M.gguf file into that folder.
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

Downloads last month: 40

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for callmechaubey/Qwen3-4B-Plus-GGUF

Base model

Qwen/Qwen3-4B-Thinking-2507

Quantized

(133)

this model