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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jc-builds/Qwen3-4B-Instruct-2507-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 jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jc-builds/Qwen3-4B-Instruct-2507-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 jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jc-builds/Qwen3-4B-Instruct-2507-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 jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jc-builds/Qwen3-4B-Instruct-2507-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 jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M

Use Docker

docker model run hf.co/jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use jc-builds/Qwen3-4B-Instruct-2507-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M

Ollama
How to use jc-builds/Qwen3-4B-Instruct-2507-GGUF with Ollama:
```
ollama run hf.co/jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
```
Unsloth Desktop

How to use jc-builds/Qwen3-4B-Instruct-2507-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jc-builds/Qwen3-4B-Instruct-2507-GGUF with Docker Model Runner:
```
docker model run hf.co/jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
```

Lemonade

How to use jc-builds/Qwen3-4B-Instruct-2507-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-4B-Instruct-2507-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use jc-builds/Qwen3-4B-Instruct-2507-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 jc-builds/Qwen3-4B-Instruct-2507-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 jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jc-builds/Qwen3-4B-Instruct-2507-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jc-builds/Qwen3-4B-Instruct-2507-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 "jc-builds/Qwen3-4B-Instruct-2507-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"

Qwen3-4B-Instruct-2507 — GGUF (iPhone-optimized)

A Q4_K_M GGUF of Qwen/Qwen3-4B-Instruct-2507 for on-device inference on 8 GB+ iPhones, iPads, and Apple Silicon Macs via llama.cpp or apps that wrap it (e.g. Haplo).

Hosted by jc-builds for the Haplo ecosystem. Quantization by Unsloth. Original weights © Alibaba Cloud, redistributed under the Apache 2.0 License; this file is a quantized (modified) version.

TL;DR

The July 2025 refresh of Qwen3-4B, released as a dedicated non-thinking instruct model: it answers directly and never emits <think> blocks, which makes it a clean fit for tool-driven agents that use plain ChatML. Strong at code for its size. See the upstream model card for benchmarks.

Available quantizations

File	Size	Recommended use
`Qwen3-4B-Instruct-2507-Q4_K_M.gguf`	2.50 GB	Default — 8 GB+ devices

Details


Parameters	4.0B (3.6B non-embedding)
Architecture	`qwen3`
Quantization	Q4_K_M
Chat format	ChatML, non-thinking only
Minimum device	8 GB RAM (iPhone 15 Pro / 16 class and newer)

How to use

Download URL:

https://huggingface.co/jc-builds/Qwen3-4B-Instruct-2507-GGUF/resolve/main/Qwen3-4B-Instruct-2507-Q4_K_M.gguf

llama.cpp

llama-cli -hf jc-builds/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M

License

Apache 2.0 (see LICENSE). Qwen3 by Alibaba Cloud — see the upstream license.

Downloads last month: 120

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for jc-builds/Qwen3-4B-Instruct-2507-GGUF

Base model

Qwen/Qwen3-4B-Instruct-2507

Quantized

(307)

this model