Instructions to use BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca with Transformers:

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

pipe = pipeline("text-generation", model="BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca")
model = AutoModelForCausalLM.from_pretrained("BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca")
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

vLLM

How to use BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca

SGLang

How to use BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca 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 "BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca" \
    --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": "BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca",
		"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 "BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca" \
        --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": "BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca with Docker Model Runner:
```
docker model run hf.co/BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen 2.5-0.5B-Instruct – French DPO

A lightweight (≈ 494 M parameters) Qwen 2.5 model fine-tuned with Direct Preference Optimization (DPO) on the AIffl/french_orca_dpo_pairs dataset. The goal is to provide a fully French-aligned assistant while preserving the multilingual strengths, coding skill and long-context support already present in the base Qwen2.5-0.5B-Instruct model.

Try it

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "BounharAbdelaziz/Qwen2.5-0.5B-DPO-French-Orca"
tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_id,
                                             torch_dtype="auto",
                                             device_map="auto")

messages = [
    {"role": "system", "content": "Vous êtes un assistant francophone serviable."},
    {"role": "user", "content": "Explique la différence entre fusion et fission nucléaires en 3 phrases."}
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
output_ids = model.generate(**tok(text, return_tensors="pt").to(model.device),
                            max_new_tokens=256)
print(tok.decode(output_ids[0], skip_special_tokens=True))

Intended use & limitations

•	Intended: French conversational agent, tutoring, summarisation, coding help in constrained contexts.
•	Not intended: Unfiltered medical, legal or financial advice; high-stakes decision making.

Although DPO reduces harmful completions, the model can still produce errors, hallucinations or biased outputs inherited from the base model and data. Always verify critical facts.