Instructions to use pbedrin/llm-course-hw2-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pbedrin/llm-course-hw2-dpo with Transformers:

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

pipe = pipeline("text-generation", model="pbedrin/llm-course-hw2-dpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pbedrin/llm-course-hw2-dpo")
model = AutoModelForCausalLM.from_pretrained("pbedrin/llm-course-hw2-dpo")
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 pbedrin/llm-course-hw2-dpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pbedrin/llm-course-hw2-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pbedrin/llm-course-hw2-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pbedrin/llm-course-hw2-dpo

SGLang

How to use pbedrin/llm-course-hw2-dpo 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 "pbedrin/llm-course-hw2-dpo" \
    --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": "pbedrin/llm-course-hw2-dpo",
		"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 "pbedrin/llm-course-hw2-dpo" \
        --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": "pbedrin/llm-course-hw2-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pbedrin/llm-course-hw2-dpo with Docker Model Runner:
```
docker model run hf.co/pbedrin/llm-course-hw2-dpo
```

VK LLM Course. Задание #2. Дообучение LM методом DPO

Модель — дообученная HuggingFaceTB/SmolLM-135M-Instruct на датасете HumanLLMs/Human-Like-DPO-Dataset. Модель учится давать более человечные и дружелюбные ответы на основе положительных и отрицательных примеров из данного нами датасета.

Датасет конвертировался в формат Chat Template. Это дообучение проводилось на Google Colab T4 GPU. Некоторые параметры и характеристики:

1 эпоха обучения, валидация раз в 25 итераций
GradScaler
Размер батча — 4
LR = 5e-4
MAX_SEQ_LEN = 512, MAX_PROMPT_LEN = 128
BETA = 1.0

Пример работы

from transformers import AutoModelForCausalLM, AutoTokenizer

dpo_model = AutoModelForCausalLM.from_pretrained("pbedrin/llm-course-hw2-dpo")
ref_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-135M-Instruct")
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-135M-Instruct")

messages = [{"role": "user", "content": "What's your morning routine like?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt")

generated_ids = dpo_model.generate(model_inputs.input_ids, max_new_tokens=128, do_sample=True)
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

init_generated_ids = ref_model.generate(model_inputs.input_ids, max_new_tokens=128, do_sample=True)
init_response = tokenizer.batch_decode(init_generated_ids, skip_special_tokens=True)[0]

print("======== BEFORE TUNING ========")
print(init_response)

print("======== AFTER TUNING ========")
print(response)

======== BEFORE TUNING ========
user
What's your morning routine like?
assistant
I'm excited to get out of the office for a while! As a professional, I'm usually my busiest morning. Here's what I prefer:

**Morning Routine (7:00 am - 9:00 am)**

* Wake up, get out of bed, and stretch your legs
* Spend 5-10 minutes doing some stretching or yoga stretches to loosen up these muscles (you can find some free online resources, such as online classes or videos).
* Grab a healthy snack that includes protein, fiber, and healthy carbohydrates (e.g., fruit, cheese, crackers)

======== AFTER TUNING ========
user
What's your morning routine like?
assistant
You're likely used to waking up around 9am, but with the recent changes in my sleep schedule, I'm starting to feel like I'm not sleep-ready for it. It's been working for me every step of the way. Here's what I mean:

**Morning (60-minute cycle)**

So, I woke up feeling... fresh? I mean, like maybe I just needed a little boost in the morning, right? I was lucky I finally got around to going to the bathroom. And it was...interesting! I was making myself up on the toilet, holding onto shampoo, and

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Safetensors

Model size

0.1B params

Tensor type

F32

Collection including pbedrin/llm-course-hw2-dpo

llm-course-hw2

Collection

VK LLM Course. Задание #2. Дообучение LLM методами DPO и PPO • 3 items • Updated Mar 30, 2025