Instructions to use obiwit/qwen2.5-3b-orpo-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use obiwit/qwen2.5-3b-orpo-mini with Transformers:

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

pipe = pipeline("text-generation", model="obiwit/qwen2.5-3b-orpo-mini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("obiwit/qwen2.5-3b-orpo-mini")
model = AutoModelForCausalLM.from_pretrained("obiwit/qwen2.5-3b-orpo-mini", 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 obiwit/qwen2.5-3b-orpo-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "obiwit/qwen2.5-3b-orpo-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "obiwit/qwen2.5-3b-orpo-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/obiwit/qwen2.5-3b-orpo-mini

SGLang

How to use obiwit/qwen2.5-3b-orpo-mini 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 "obiwit/qwen2.5-3b-orpo-mini" \
    --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": "obiwit/qwen2.5-3b-orpo-mini",
		"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 "obiwit/qwen2.5-3b-orpo-mini" \
        --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": "obiwit/qwen2.5-3b-orpo-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use obiwit/qwen2.5-3b-orpo-mini with Docker Model Runner:
```
docker model run hf.co/obiwit/qwen2.5-3b-orpo-mini
```

qwen2.5-3b-orpo-mini / all_results.json

obiwit

End of training

fbbdaa6 verified about 1 year ago

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	{
	"epoch": 0.9999354130336499,
	"eval_log_odds_chosen": 1.242581844329834,
	"eval_log_odds_ratio": -0.47447991371154785,
	"eval_logits/chosen": -1.2279024124145508,
	"eval_logits/rejected": -1.0133243799209595,
	"eval_logps/chosen": -0.8291401863098145,
	"eval_logps/rejected": -1.7743595838546753,
	"eval_loss": 1.2853127717971802,
	"eval_nll_loss": 1.246997594833374,
	"eval_rewards/accuracies": 0.7431685924530029,
	"eval_rewards/chosen": -0.0829220786690712,
	"eval_rewards/margins": 0.09452750533819199,
	"eval_rewards/rejected": -0.1774357259273529,
	"eval_runtime": 905.5189,
	"eval_samples": 54974,
	"eval_samples_per_second": 60.743,
	"eval_steps_per_second": 0.95,
	"total_flos": 0.0,
	"train_loss": 0.9929802710657941,
	"train_runtime": 57443.5833,
	"train_samples": 990885,
	"train_samples_per_second": 17.25,
	"train_steps_per_second": 0.135
	}