Instructions to use Dqdung205/qwen-function-calling-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dqdung205/qwen-function-calling-model with Transformers:

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

pipe = pipeline("text-generation", model="Dqdung205/qwen-function-calling-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Dqdung205/qwen-function-calling-model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dqdung205/qwen-function-calling-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dqdung205/qwen-function-calling-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dqdung205/qwen-function-calling-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dqdung205/qwen-function-calling-model

SGLang

How to use Dqdung205/qwen-function-calling-model 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 "Dqdung205/qwen-function-calling-model" \
    --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": "Dqdung205/qwen-function-calling-model",
		"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 "Dqdung205/qwen-function-calling-model" \
        --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": "Dqdung205/qwen-function-calling-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Dqdung205/qwen-function-calling-model 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 Dqdung205/qwen-function-calling-model 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 Dqdung205/qwen-function-calling-model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dqdung205/qwen-function-calling-model to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Dqdung205/qwen-function-calling-model",
    max_seq_length=2048,
)

Docker Model Runner
How to use Dqdung205/qwen-function-calling-model with Docker Model Runner:
```
docker model run hf.co/Dqdung205/qwen-function-calling-model
```

🧠 Qwen Function-Calling (Vietnamese Finetune)

Model này là một phiên bản Qwen-based model được finetune bằng Unsloth
trên dữ liệu hội thoại tiếng Việt có khả năng function-calling từ bộ dữ liệu: 👉 5CD-AI/Vietnamese-Locutusque-function-calling-chatml-gg-translated

🧩 Model Details

Base model: Qwen/Qwen2.5-7B-Instruct
Finetuning framework: Unsloth
Task: Text Generation + Function Calling
Language: Vietnamese 🇻🇳
Architecture: Decoder-only Transformer (Causal LM)
License: MIT
Finetuned from: Qwen base model

🚀 How to Use

Inference Example (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "dungai/qwen-function-calling"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Xin chào! Hãy gọi hàm `get_weather` với tham số thành phố là Hà Nội."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: -; Downloads are not tracked for this model. How to track