Instructions to use magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF",
	filename="Qwen3-4B-Instruct-2507-IQ4_NL.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL

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 magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL

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 magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL

Use Docker

docker model run hf.co/magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL

LM Studio
Jan

vLLM

How to use magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-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": "magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL

Ollama
How to use magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF with Ollama:
```
ollama run hf.co/magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL
```

Unsloth Studio

How to use magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-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 magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-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 magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF to start chatting

How to use magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL

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": "magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL

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 magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL

Run Hermes

hermes

Docker Model Runner
How to use magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF with Docker Model Runner:
```
docker model run hf.co/magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL
```

Lemonade

How to use magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull magiccodingman/Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF:IQ4_NL

Run and chat with the model

lemonade run user.Qwen3-4B-Instruct-2507-Unsloth-MagicQuant-Hybrid-GGUF-IQ4_NL

List all available models

lemonade list

MagicQuant GGUF Hybrids - Qwen3 4B Instruct 2507

(DEPRECIATED - Part of MagicQuant v1.0 which had significant flaws. Please utilize v2.0 which is production ready)

MagicQuant is an automated quantization, benchmarking, and evolutionary hybrid-GGUF search system for LLMs.

Each release includes models optimized to outperform standard baseline quants (Q8, Q6, Q5, Q4). If a baseline GGUF exists in this repo, the evolutionary engine couldn’t beat it. If a baseline is missing, it’s because a hybrid configuration outperformed it so completely that including the baseline would've been pointless.

These hybrid GGUFs are built to be as small, fast, and low-drift as possible while preserving model capability.

To dive deeper into how MagicQuant works, see the main repo: MagicQuant on GitHub (by MagicCodingMan)

Notes:

The HuggingFace hardware compatibility where it shows the bits is usually wrong. It doesn't understand hybrid mixes, so don't trust it.
Naming scheme can be found on the MagicQuant Wiki.
(tips) Less precision loss means less brain damage. More TPS means faster! Smaller is always better right?

Precision Loss Guide

0–0.1% → God-tier, scientifically exact
0.1–1% → True near-lossless, agent-ready
1–3% → Minimal loss, great for personal use
3–5% → Borderline, but still functional
5%+ → Toys, not tools, outside MagicQuant’s scope

Learn more about precision loss here.

Table - File Size + TPS + Avg Precision Loss

model_name	file_size_gb	bench_tps	avg_prec_loss
mxfp4_moe-K-B16-QO-Q6K-EUD-Q8_0	3.98	373.48	0.0533%
mxfp4_moe-O-Q5K-EQKUD-Q6K	3.03	428.37	0.1631%
mxfp4_moe-QUD-IQ4NL-KO-MXFP4-E-Q8_0	2.28	411.49	0.7356%
mxfp4_moe-K-B16-QU-IQ4NL-O-MXFP4-E-Q5K-D-Q6K	2.62	467.79	0.8322%
IQ4_NL	2.23	426.86	0.8996%
mxfp4_moe-EQUD-IQ4NL-KO-MXFP4	2.10	518.15	2.0904%

Table - PPL Columns

model_name	gen	gen_er	code	code_er	math	math_er
mxfp4_moe-K-B16-QO-Q6K-EUD-Q8_0	8.8766	0.2053	1.5463	0.0122	6.7119	0.1368
mxfp4_moe-O-Q5K-EQKUD-Q6K	8.8564	0.2036	1.5473	0.0122	6.6976	0.1358
mxfp4_moe-QUD-IQ4NL-KO-MXFP4-E-Q8_0	9.0127	0.2057	1.5546	0.0119	6.6919	0.1331
mxfp4_moe-K-B16-QU-IQ4NL-O-MXFP4-E-Q5K-D-Q6K	9.0490	0.2096	1.5535	0.0121	6.7221	0.1358
IQ4_NL	8.9948	0.2072	1.5600	0.0123	6.7484	0.1362
mxfp4_moe-EQUD-IQ4NL-KO-MXFP4	9.2104	0.2106	1.5598	0.0119	6.8261	0.1363

Table - Precision Loss Columns

model_name	loss_general	loss_code	loss_math
mxfp4_moe-K-B16-QO-Q6K-EUD-Q8_0	0.0720	0.0388	0.0492
mxfp4_moe-O-Q5K-EQKUD-Q6K	0.2994	0.0259	0.1640
mxfp4_moe-QUD-IQ4NL-KO-MXFP4-E-Q8_0	1.4601	0.4978	0.2489
mxfp4_moe-K-B16-QU-IQ4NL-O-MXFP4-E-Q5K-D-Q6K	1.8687	0.4267	0.2012
IQ4_NL	1.2586	0.8469	0.5933
mxfp4_moe-EQUD-IQ4NL-KO-MXFP4	3.6857	0.8339	1.7515

Baseline Models (Reference)

Table - File Size + TPS + Avg Precision Loss

model_name	file_size_gb	bench_tps	avg_prec_loss
BF16	7.50	254.70	0.0000%
Q8_0	3.99	362.48	0.0724%
Q6_K	3.08	397.92	0.2492%
Q5_K	2.69	385.17	0.7920%
IQ4_NL	2.23	426.86	0.8996%
Q4_K_M	2.33	377.19	0.9376%
MXFP4_MOE	2.00	467.13	8.2231%

Table - PPL Columns

model_name	gen	gen_er	code	code_er	math	math_er
BF16	8.8830	0.2056	1.5469	0.0122	6.7086	0.1369
Q8_0	8.8754	0.2053	1.5488	0.0123	6.7080	0.1367
Q6_K	8.8441	0.2034	1.5452	0.0121	6.6952	0.1357
Q5_K	8.9707	0.2079	1.5542	0.0123	6.7701	0.1384
IQ4_NL	8.9948	0.2072	1.5600	0.0123	6.7484	0.1362
Q4_K_M	8.9446	0.2051	1.5694	0.0125	6.7532	0.1371
MXFP4_MOE	9.8799	0.2282	1.6122	0.0130	7.3275	0.1494

Table - Precision Loss Columns

model_name	loss_general	loss_code	loss_math
BF16	0.0000	0.0000	0.0000
Q8_0	0.0856	0.1228	0.0089
Q6_K	0.4379	0.1099	0.1997
Q5_K	0.9873	0.4719	0.9167
IQ4_NL	1.2586	0.8469	0.5933
Q4_K_M	0.6935	1.4545	0.6648
MXFP4_MOE	11.2226	4.2213	9.2255

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