Instructions to use emibrahim/Qwen2.5-0.5B-NLP-Project-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use emibrahim/Qwen2.5-0.5B-NLP-Project-2 with Transformers:

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

pipe = pipeline("text-generation", model="emibrahim/Qwen2.5-0.5B-NLP-Project-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("emibrahim/Qwen2.5-0.5B-NLP-Project-2")
model = AutoModelForMultimodalLM.from_pretrained("emibrahim/Qwen2.5-0.5B-NLP-Project-2")
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 emibrahim/Qwen2.5-0.5B-NLP-Project-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "emibrahim/Qwen2.5-0.5B-NLP-Project-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emibrahim/Qwen2.5-0.5B-NLP-Project-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/emibrahim/Qwen2.5-0.5B-NLP-Project-2

SGLang

How to use emibrahim/Qwen2.5-0.5B-NLP-Project-2 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 "emibrahim/Qwen2.5-0.5B-NLP-Project-2" \
    --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": "emibrahim/Qwen2.5-0.5B-NLP-Project-2",
		"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 "emibrahim/Qwen2.5-0.5B-NLP-Project-2" \
        --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": "emibrahim/Qwen2.5-0.5B-NLP-Project-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use emibrahim/Qwen2.5-0.5B-NLP-Project-2 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 emibrahim/Qwen2.5-0.5B-NLP-Project-2 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 emibrahim/Qwen2.5-0.5B-NLP-Project-2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for emibrahim/Qwen2.5-0.5B-NLP-Project-2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="emibrahim/Qwen2.5-0.5B-NLP-Project-2",
    max_seq_length=2048,
)

Docker Model Runner
How to use emibrahim/Qwen2.5-0.5B-NLP-Project-2 with Docker Model Runner:
```
docker model run hf.co/emibrahim/Qwen2.5-0.5B-NLP-Project-2
```

Uploaded finetuned model

This model is a fine-tuned version of Qwen2.5-0.5B-Instruct optimized for strict instruction-following and concise, human-aligned responses. It was trained using a two-stage alignment pipeline:

Supervised Fine-Tuning (SFT): Trained on the HuggingFaceH4/no_robots dataset to improve structural formatting and zero-shot task execution.

Direct Preference Optimization (DPO): Aligned using the argilla/dpo-mix-7k dataset to reduce conversational verbosity and prioritize helpful, direct answers.

The model utilizes PEFT/LoRA adapters and was trained efficiently within memory constraints using the Unsloth framework.

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 36

Safetensors

Model size

0.5B params

Tensor type

BF16