Instructions to use marcusmi4n/qwen2.5-0.5b-conversion-ready with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marcusmi4n/qwen2.5-0.5b-conversion-ready with Transformers:

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

pipe = pipeline("text-generation", model="marcusmi4n/qwen2.5-0.5b-conversion-ready")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("marcusmi4n/qwen2.5-0.5b-conversion-ready")
model = AutoModelForCausalLM.from_pretrained("marcusmi4n/qwen2.5-0.5b-conversion-ready")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use marcusmi4n/qwen2.5-0.5b-conversion-ready with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marcusmi4n/qwen2.5-0.5b-conversion-ready"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marcusmi4n/qwen2.5-0.5b-conversion-ready",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/marcusmi4n/qwen2.5-0.5b-conversion-ready

SGLang

How to use marcusmi4n/qwen2.5-0.5b-conversion-ready 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 "marcusmi4n/qwen2.5-0.5b-conversion-ready" \
    --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": "marcusmi4n/qwen2.5-0.5b-conversion-ready",
		"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 "marcusmi4n/qwen2.5-0.5b-conversion-ready" \
        --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": "marcusmi4n/qwen2.5-0.5b-conversion-ready",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use marcusmi4n/qwen2.5-0.5b-conversion-ready with Docker Model Runner:
```
docker model run hf.co/marcusmi4n/qwen2.5-0.5b-conversion-ready
```

Qwen 2.5 0.5B - Conversion Ready

This repository contains a Qwen 2.5 0.5B model prepared for ONNX conversion and QNN deployment.

Model Details

Base Model: Qwen/Qwen2.5-0.5B
Architecture: Qwen2ForCausalLM
Parameters: ~0.5B
Hidden Size: 896
Layers: 24
Vocabulary Size: 151936
Max Position: 32768

Features

✅ PyTorch Model: Ready to use with transformers
✅ Conversion Scripts: Includes ONNX conversion utilities
✅ QNN Ready: Optimized for Qualcomm Neural Network SDK
✅ Documentation: Complete conversion pipeline docs

Usage

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("marcusmi4n/qwen2.5-0.5b-conversion-ready")
tokenizer = AutoTokenizer.from_pretrained("marcusmi4n/qwen2.5-0.5b-conversion-ready")

# Generate text
inputs = tokenizer("Hello, I am", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

ONNX Conversion

# Use the included conversion script
python convert_to_onnx.py --model-path marcusmi4n/qwen2.5-0.5b-conversion-ready --output-path model.onnx

Known Issues

Direct ONNX export may face challenges due to modern attention mechanisms
Recommend using the included conversion scripts for best results
For QNN deployment, consider the full pipeline in the repository

Files Included

pytorch_model.bin / model.safetensors - Model weights
config.json - Model configuration
tokenizer.json - Tokenizer
convert_to_onnx.py - ONNX conversion script
README.md - This documentation
conversion_report.md - Detailed conversion analysis

License

Apache 2.0 - Same as base Qwen 2.5 model

Citation

@misc{qwen25-conversion-ready,
  title={Qwen 2.5 0.5B - Conversion Ready},
  author={QNN Conversion Pipeline},
  year={2025},
  url={https://huggingface.co/marcusmi4n/qwen2.5-0.5b-conversion-ready}
}

Base Model Citation

Please cite the original Qwen 2.5 paper and model:

@article{qwen2.5,
  title={Qwen2.5: A Party of Foundation Models},
  author={Qwen Team},
  journal={arXiv preprint},
  year={2024}
}

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Safetensors

Model size

0.5B params

Tensor type

BF16

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