Instructions to use obiwit/llama3.2-3b-orpo-finegrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use obiwit/llama3.2-3b-orpo-finegrained with Transformers:

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

pipe = pipeline("text-generation", model="obiwit/llama3.2-3b-orpo-finegrained")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("obiwit/llama3.2-3b-orpo-finegrained")
model = AutoModelForCausalLM.from_pretrained("obiwit/llama3.2-3b-orpo-finegrained", 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/llama3.2-3b-orpo-finegrained with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "obiwit/llama3.2-3b-orpo-finegrained"
# 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/llama3.2-3b-orpo-finegrained",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/obiwit/llama3.2-3b-orpo-finegrained

SGLang

How to use obiwit/llama3.2-3b-orpo-finegrained 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/llama3.2-3b-orpo-finegrained" \
    --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/llama3.2-3b-orpo-finegrained",
		"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/llama3.2-3b-orpo-finegrained" \
        --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/llama3.2-3b-orpo-finegrained",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use obiwit/llama3.2-3b-orpo-finegrained with Docker Model Runner:
```
docker model run hf.co/obiwit/llama3.2-3b-orpo-finegrained
```

llama3.2-3b-orpo-finegrained

File size: 731 Bytes

c23f2c7

{
    "epoch": 0.9999635926748461,
    "eval_log_odds_chosen": 1.1701252460479736,
    "eval_log_odds_ratio": -0.47759079933166504,
    "eval_logits/chosen": -0.8645420074462891,
    "eval_logits/rejected": -0.7723644971847534,
    "eval_logps/chosen": -0.7588062882423401,
    "eval_logps/rejected": -1.6227697134017944,
    "eval_loss": 1.188714861869812,
    "eval_nll_loss": 1.136938452720642,
    "eval_rewards/accuracies": 0.7338129281997681,
    "eval_rewards/chosen": -0.07588158547878265,
    "eval_rewards/margins": 0.08640365302562714,
    "eval_rewards/rejected": -0.16227255761623383,
    "eval_runtime": 1369.8995,
    "eval_samples": 98376,
    "eval_samples_per_second": 71.429,
    "eval_steps_per_second": 1.116
}