Instructions to use qgallouedec/online-dpo-qwen2-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qgallouedec/online-dpo-qwen2-4 with Transformers:

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

pipe = pipeline("text-generation", model="qgallouedec/online-dpo-qwen2-4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("qgallouedec/online-dpo-qwen2-4")
model = AutoModelForCausalLM.from_pretrained("qgallouedec/online-dpo-qwen2-4", device_map="auto")
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 qgallouedec/online-dpo-qwen2-4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qgallouedec/online-dpo-qwen2-4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qgallouedec/online-dpo-qwen2-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qgallouedec/online-dpo-qwen2-4

SGLang

How to use qgallouedec/online-dpo-qwen2-4 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 "qgallouedec/online-dpo-qwen2-4" \
    --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": "qgallouedec/online-dpo-qwen2-4",
		"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 "qgallouedec/online-dpo-qwen2-4" \
        --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": "qgallouedec/online-dpo-qwen2-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use qgallouedec/online-dpo-qwen2-4 with Docker Model Runner:
```
docker model run hf.co/qgallouedec/online-dpo-qwen2-4
```

Model Card for online-dpo-qwen2-4

This model is a fine-tuned version of Qwen/Qwen2-0.5B-Instruct on the trl-lib/ultrafeedback-prompt dataset. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="qgallouedec/online-dpo-qwen2-4", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=500)[0]
print(output["generated_text"][1]["content"])

Training procedure

This model was trained with Online DPO, a method introduced in Direct Language Model Alignment from Online AI Feedback.

Framework versions

TRL: 0.12.0.dev0
Transformers: 4.45.0.dev0
Pytorch: 2.4.1
Datasets: 3.0.0
Tokenizers: 0.19.1

Downloads last month: 3

Safetensors

Model size

0.5B params

Tensor type

F32

Model tree for qgallouedec/online-dpo-qwen2-4

Base model

Qwen/Qwen2-0.5B

Finetuned

Qwen/Qwen2-0.5B-Instruct

Finetuned

(592)

this model

Dataset used to train qgallouedec/online-dpo-qwen2-4

Paper for qgallouedec/online-dpo-qwen2-4

Direct Language Model Alignment from Online AI Feedback

Paper • 2402.04792 • Published Feb 7, 2024 • 36