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

Libraries

How to use prithivMLmods/Qwen3-4B-Thinking-2507-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Qwen3-4B-Thinking-2507-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Qwen3-4B-Thinking-2507-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Qwen3-4B-Thinking-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 prithivMLmods/Qwen3-4B-Thinking-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3-4B-Thinking-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 prithivMLmods/Qwen3-4B-Thinking-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3-4B-Thinking-2507-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf prithivMLmods/Qwen3-4B-Thinking-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf prithivMLmods/Qwen3-4B-Thinking-2507-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-4B-Thinking-2507-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use prithivMLmods/Qwen3-4B-Thinking-2507-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-4B-Thinking-2507-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Qwen3-4B-Thinking-2507-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Qwen3-4B-Thinking-2507-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-4B-Thinking-2507-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Qwen3-4B-Thinking-2507-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-4B-Thinking-2507-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

How to use prithivMLmods/Qwen3-4B-Thinking-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 prithivMLmods/Qwen3-4B-Thinking-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": "prithivMLmods/Qwen3-4B-Thinking-2507-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use prithivMLmods/Qwen3-4B-Thinking-2507-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen3-4B-Thinking-2507-GGUF:Q4_K_M

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/Qwen3-4B-Thinking-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 prithivMLmods/Qwen3-4B-Thinking-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 prithivMLmods/Qwen3-4B-Thinking-2507-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/Qwen3-4B-Thinking-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 prithivMLmods/Qwen3-4B-Thinking-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 "prithivMLmods/Qwen3-4B-Thinking-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-Thinking-2507-GGUF

Qwen3-4B-Thinking-2507 is a 4-billion-parameter causal language model specialized for advanced thinking and reasoning tasks, featuring significant improvements in logical reasoning, mathematics, science, coding, and academic benchmarks typically requiring human expertise. It supports an extremely long native context length of 262,144 tokens, enabling deep and complex problem-solving. Unlike prior versions, this model operates solely in "thinking mode," automatically generating intermediate reasoning steps enclosed in special tags to enhance transparency and interpretability of its outputs. It has 36 layers, uses a GQA attention mechanism with 32 query heads and 8 key-value heads, and benefits from pretraining and post-training stages to optimize both quality and depth of reasoning.

Qwen3-4B-Thinking-2507 also improves general capabilities such as instruction following, tool usage, text generation, and alignment with human preferences, making it suitable for highly complex reasoning tasks in scientific, coding, and academic domains. The model is fully integrated with the Hugging Face transformers library and can be deployed with extended context support via toolkits like vLLM and SGLang. Detailed benchmarks, deployment guidelines, and usage examples are available in its official repository, making it an excellent choice for research and applications requiring transparent, scalable AI reasoning.

Model Files

File Name	Size	Quant Type
Qwen3-4B-Thinking-2507.BF16.gguf	8.05 GB	BF16
Qwen3-4B-Thinking-2507.F16.gguf	8.05 GB	F16
Qwen3-4B-Thinking-2507.F32.gguf	16.1 GB	F32
Qwen3-4B-Thinking-2507.Q2_K.gguf	1.67 GB	Q2_K
Qwen3-4B-Thinking-2507.Q3_K_L.gguf	2.24 GB	Q3_K_L
Qwen3-4B-Thinking-2507.Q3_K_M.gguf	2.08 GB	Q3_K_M
Qwen3-4B-Thinking-2507.Q3_K_S.gguf	1.89 GB	Q3_K_S
Qwen3-4B-Thinking-2507.Q4_K_M.gguf	2.5 GB	Q4_K_M
Qwen3-4B-Thinking-2507.Q4_K_S.gguf	2.38 GB	Q4_K_S
Qwen3-4B-Thinking-2507.Q5_K_M.gguf	2.89 GB	Q5_K_M
Qwen3-4B-Thinking-2507.Q5_K_S.gguf	2.82 GB	Q5_K_S
Qwen3-4B-Thinking-2507.Q6_K.gguf	3.31 GB	Q6_K
Qwen3-4B-Thinking-2507.Q8_0.gguf	4.28 GB	Q8_0

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 505

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/Qwen3-4B-Thinking-2507-GGUF

Base model

Qwen/Qwen3-4B-Thinking-2507

Quantized

(145)

this model

Collection including prithivMLmods/Qwen3-4B-Thinking-2507-GGUF

Qwen3-4B-2507-GGUF

Collection

max - f32 • 4 items • Updated 1 day ago • 1