Instructions to use Mustafaege/Qwen3-VL-4B-tool-calling-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mustafaege/Qwen3-VL-4B-tool-calling-ft with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-vl-4b-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Mustafaege/Qwen3-VL-4B-tool-calling-ft")

Transformers

How to use Mustafaege/Qwen3-VL-4B-tool-calling-ft with Transformers:

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

pipe = pipeline("text-generation", model="Mustafaege/Qwen3-VL-4B-tool-calling-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Mustafaege/Qwen3-VL-4B-tool-calling-ft", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mustafaege/Qwen3-VL-4B-tool-calling-ft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mustafaege/Qwen3-VL-4B-tool-calling-ft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mustafaege/Qwen3-VL-4B-tool-calling-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mustafaege/Qwen3-VL-4B-tool-calling-ft

SGLang

How to use Mustafaege/Qwen3-VL-4B-tool-calling-ft 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 "Mustafaege/Qwen3-VL-4B-tool-calling-ft" \
    --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": "Mustafaege/Qwen3-VL-4B-tool-calling-ft",
		"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 "Mustafaege/Qwen3-VL-4B-tool-calling-ft" \
        --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": "Mustafaege/Qwen3-VL-4B-tool-calling-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Mustafaege/Qwen3-VL-4B-tool-calling-ft 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 Mustafaege/Qwen3-VL-4B-tool-calling-ft 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 Mustafaege/Qwen3-VL-4B-tool-calling-ft to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mustafaege/Qwen3-VL-4B-tool-calling-ft to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Mustafaege/Qwen3-VL-4B-tool-calling-ft",
    max_seq_length=2048,
)

Docker Model Runner
How to use Mustafaege/Qwen3-VL-4B-tool-calling-ft with Docker Model Runner:
```
docker model run hf.co/Mustafaege/Qwen3-VL-4B-tool-calling-ft
```

Qwen3-VL-4B Tool Calling Fine-tune

A LoRA adapter fine-tuned on top of unsloth/qwen3-vl-4b-instruct-bnb-4bit for tool calling / function calling tasks. The model supports structured <tool_call> / <tool_response> XML format and can handle both text and image inputs.

Model Details

Property	Value
Base Model	`unsloth/qwen3-vl-4b-instruct-bnb-4bit`
Model Type	Vision-Language (Qwen3-VL), Causal LM
Fine-tune Method	LoRA (PEFT)
LoRA Rank (r)	16
LoRA Alpha	16
LoRA Dropout	0
Target Modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Adapter Size	~143 MB
PEFT Version	0.18.1
License	Apache 2.0
Developed by	Mustafaege

Intended Use

This model is designed for agentic and tool-use pipelines where an LLM needs to:

Select and invoke tools/functions based on user queries
Parse tool responses and continue reasoning
Handle multimodal inputs (text + images) alongside tool use

Direct Use

Load the adapter and use it for tool-calling inference in any framework that supports PEFT/LoRA.

Out-of-Scope Use

General-purpose chat without tool schemas (use the base instruct model instead)
Tasks requiring models larger than 4B parameters for complex reasoning

How to Get Started

Installation

pip install transformers peft unsloth torch

Basic Inference

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "unsloth/qwen3-vl-4b-instruct-bnb-4bit"
adapter_id = "Mustafaege/Qwen3-VL-4B-tool-calling-ft"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()

Tool Calling Example

tools = [
    {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City name"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location"]
        }
    }
]

messages = [
    {"role": "user", "content": "What's the weather like in Istanbul?"}
]

text = tokenizer.apply_chat_template(
    messages,
    tools=tools,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)
# Expected: <tool_call>
# {"name": "get_weather", "arguments": {"location": "Istanbul", "unit": "celsius"}}
# </tool_call>

Using with Unsloth (Faster Inference)

from unsloth import FastVisionModel

model, tokenizer = FastVisionModel.from_pretrained(
    model_name="Mustafaege/Qwen3-VL-4B-tool-calling-ft",
    load_in_4bit=True,
)
FastVisionModel.for_inference(model)

Tool Call Format

The model uses XML-style tags for tool interactions:

Tool invocation (model output):

<tool_call>
{"name": "function_name", "arguments": {"param1": "value1"}}
</tool_call>

Tool response (user message):

<tool_response>
{"result": "..."}
</tool_response>

Training Details

Training Data

Fine-tuned on a custom tool-calling dataset covering diverse function schemas and multi-turn tool-use conversations.

Training Procedure

Framework: Unsloth + PEFT
Method: LoRA (Low-Rank Adaptation)
Base quantization: 4-bit (BitsAndBytes NF4)
Training regime: bf16 mixed precision

Limitations & Bias

Performance depends heavily on the quality and coverage of the tool schemas provided
May hallucinate function calls for tools not present in the schema
Vision capabilities are inherited from the base model; visual grounding for tool calling is experimental
Limited to the tool-calling patterns seen in training data

Citation

If you use this model, please cite the base model:

@misc{qwen3vl,
  title={Qwen3-VL Technical Report},
  author={Qwen Team},
  year={2025},
  publisher={Alibaba Cloud}
}

Framework Versions

PEFT 0.18.1
Transformers ≥ 4.51.0

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