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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Qwen/Qwen3-4B-GGUF",
	filename="Qwen3-4B-Q4_K_M.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 Qwen/Qwen3-4B-GGUF with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

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

Unsloth Studio new

How to use Qwen/Qwen3-4B-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 Qwen/Qwen3-4B-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 Qwen/Qwen3-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Qwen/Qwen3-4B-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": "Qwen/Qwen3-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Qwen/Qwen3-4B-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 Qwen/Qwen3-4B-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 Qwen/Qwen3-4B-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

feihu.hf commited on May 9, 2025

Commit

6329251

1 Parent(s): 34778e2

update README

Browse files

Files changed (2) hide show

README.md +2 -2
params +2 -1

README.md CHANGED Viewed

@@ -30,7 +30,7 @@ Qwen3 is the latest generation of large language models in Qwen series, offering
 - Number of Paramaters (Non-Embedding): 3.6B
 - Number of Layers: 36
 - Number of Attention Heads (GQA): 32 for Q and 8 for KV
-- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
 - Quantization: q4_K_M, q5_0, q5_K_M, q6_K, q8_0
@@ -46,7 +46,7 @@ We advise you to clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and
 In the following demonstration, we assume that you are running commands under the repository `llama.cpp`.
 ```shell
-./llama-cli -hf Qwen/Qwen3-4B:Q8_0 --jinja --color -ngl 99 -fa -sm row --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 --presence-penalty 1.5 -c 40960 -n 32768 --no-context-shift
 ```
 ### ollama

 - Number of Paramaters (Non-Embedding): 3.6B
 - Number of Layers: 36
 - Number of Attention Heads (GQA): 32 for Q and 8 for KV
+- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
 - Quantization: q4_K_M, q5_0, q5_K_M, q6_K, q8_0
 In the following demonstration, we assume that you are running commands under the repository `llama.cpp`.
 ```shell
+./llama-cli -hf Qwen/Qwen3-4B-GGUF:Q8_0 --jinja --color -ngl 99 -fa -sm row --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 --presence-penalty 1.5 -c 40960 -n 32768 --no-context-shift
 ```
 ### ollama

params CHANGED Viewed

@@ -9,5 +9,6 @@
     "presence_penalty" : 1.5,
     "top_k" : 20,
     "top_p" : 0.95,
-    "num_predict" : 32768
 }

     "presence_penalty" : 1.5,
     "top_k" : 20,
     "top_p" : 0.95,
+    "num_predict" : 32768,
+    "num_ctx": 40960
 }