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

Libraries

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

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

pipe = pipeline("text-generation", model="prithivMLmods/Qwen3-4B-Instruct-2507-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Qwen3-4B-Instruct-2507-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Qwen3-4B-Instruct-2507-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Qwen3-4B-Instruct-2507-GGUF",
	filename="Qwen3-4B-Instruct-2507.BF16.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 prithivMLmods/Qwen3-4B-Instruct-2507-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/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-server -hf prithivMLmods/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/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 prithivMLmods/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/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 prithivMLmods/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use prithivMLmods/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 "prithivMLmods/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": "prithivMLmods/Qwen3-4B-Instruct-2507-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use prithivMLmods/Qwen3-4B-Instruct-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-Instruct-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-Instruct-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-Instruct-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-Instruct-2507-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio

How to use prithivMLmods/Qwen3-4B-Instruct-2507-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 prithivMLmods/Qwen3-4B-Instruct-2507-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 prithivMLmods/Qwen3-4B-Instruct-2507-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/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-server -hf prithivMLmods/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 prithivMLmods/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/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

Qwen3-4B-Instruct-2507-GGUF

Qwen3-4B-Instruct-2507 is a 4-billion-parameter causal language model designed for advanced instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage, with significant improvements in these general capabilities and substantial gains in long-tail knowledge coverage across multiple languages. It operates in a non-thinking mode without generating explicit reasoning step tags, features 36 layers, 32 query and 8 key-value attention heads using GQA, and supports an extremely long native context length of 262,144 tokens.

The model is pretrained and post-trained for enhanced performance and alignment with user preferences, excelling in subjective and open-ended tasks with more helpful and higher-quality responses. It can be deployed efficiently via popular toolkits such as Hugging Face Transformers, SGLang, and vLLM, and supports extensive long-context understanding suitable for complex tasks. Code examples, benchmark evaluations, deployment instructions, and agentic tool-calling capabilities with Qwen-Agent are fully documented in its official repository on Hugging Face.

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: 302

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-Instruct-2507-GGUF

Base model

Qwen/Qwen3-4B-Instruct-2507

Quantized

(248)

this model

Spaces using prithivMLmods/Qwen3-4B-Instruct-2507-GGUF 2

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

Qwen3-4B-2507-GGUF

Collection

max - f32 • 4 items • Updated Apr 27 • 1