Instructions to use beachcities/Qwen3-4B-DPO-Final-Day3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beachcities/Qwen3-4B-DPO-Final-Day3 with Transformers:

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

pipe = pipeline("text-generation", model="beachcities/Qwen3-4B-DPO-Final-Day3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beachcities/Qwen3-4B-DPO-Final-Day3")
model = AutoModelForCausalLM.from_pretrained("beachcities/Qwen3-4B-DPO-Final-Day3", 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 beachcities/Qwen3-4B-DPO-Final-Day3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beachcities/Qwen3-4B-DPO-Final-Day3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beachcities/Qwen3-4B-DPO-Final-Day3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beachcities/Qwen3-4B-DPO-Final-Day3

SGLang

How to use beachcities/Qwen3-4B-DPO-Final-Day3 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 "beachcities/Qwen3-4B-DPO-Final-Day3" \
    --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": "beachcities/Qwen3-4B-DPO-Final-Day3",
		"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 "beachcities/Qwen3-4B-DPO-Final-Day3" \
        --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": "beachcities/Qwen3-4B-DPO-Final-Day3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use beachcities/Qwen3-4B-DPO-Final-Day3 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for beachcities/Qwen3-4B-DPO-Final-Day3 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for beachcities/Qwen3-4B-DPO-Final-Day3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for beachcities/Qwen3-4B-DPO-Final-Day3 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="beachcities/Qwen3-4B-DPO-Final-Day3",
    max_seq_length=2048,
)

Docker Model Runner
How to use beachcities/Qwen3-4B-DPO-Final-Day3 with Docker Model Runner:
```
docker model run hf.co/beachcities/Qwen3-4B-DPO-Final-Day3
```

Qwen3-4B-Instruct DPO Finetuned (Day 3 Final)

This model is a fine-tuned version of unsloth/Qwen3-4B-Instruct-2507 using Direct Preference Optimization (DPO) via the Unsloth library.

This repository contains the full-merged 16-bit weights. No adapter loading is required.

⚠️ Note on Chat Template / URL

The repository URL and training configuration contain qwen-2.5. This was selected solely for technical compatibility (as Qwen 3 shares the same ChatML format), and does NOT indicate the use of the Qwen 2.5 base model. The base model used is strictly Qwen3-4B-Instruct-2507.

Training Configuration

Compliance Note: The training base model strictly follows the competition rule §6.6.
Base Model: unsloth/Qwen3-4B-Instruct-2507 (Authorized Model)
Method: DPO (Direct Preference Optimization)
Dataset: Custom Tier-Strict++ Dataset
Epochs: 3
Learning Rate: 5e-6
Max Length: 2048
Hardware: NVIDIA A100 (Google Colab)

Usage

Since this is a merged model, you can use it directly with transformers.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Note: The URL contains '2.5' due to initial template settings, but the model is Qwen3.
model_id = "beachcities/Qwen2.5-3B-DPO-Final-Day3" 

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Test inference
prompt = "指示: 次の質問に論理的に答えてください。\\n質問: AIの未来についてどう思いますか？"
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 6

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for beachcities/Qwen3-4B-DPO-Final-Day3

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

unsloth/Qwen3-4B-Instruct-2507

Finetuned

(422)

this model