Instructions to use qingy2024/UwU-14B-Math-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qingy2024/UwU-14B-Math-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="qingy2024/UwU-14B-Math-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("qingy2024/UwU-14B-Math-v0.2")
model = AutoModelForCausalLM.from_pretrained("qingy2024/UwU-14B-Math-v0.2", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use qingy2024/UwU-14B-Math-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qingy2024/UwU-14B-Math-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qingy2024/UwU-14B-Math-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qingy2024/UwU-14B-Math-v0.2

SGLang

How to use qingy2024/UwU-14B-Math-v0.2 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 "qingy2024/UwU-14B-Math-v0.2" \
    --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": "qingy2024/UwU-14B-Math-v0.2",
		"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 "qingy2024/UwU-14B-Math-v0.2" \
        --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": "qingy2024/UwU-14B-Math-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use qingy2024/UwU-14B-Math-v0.2 with Docker Model Runner:
```
docker model run hf.co/qingy2024/UwU-14B-Math-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded model

Developed by: qingy2024
License: apache-2.0
Finetuned from model : unsloth/qwen2.5-14b-bnb-4bit

This model is a fine-tuned version of Qwen 2.5-14B, trained on QwQ 32B Preview's responses to questions from the NuminaMathCoT dataset.

Note: This model uses the standard ChatML template.

At 500 steps, the loss was plateauing so I decided to stop training to prevent excessive overfitting.

Training Details

Base Model: Qwen 2.5-14B
Fine-Tuning Dataset: Verified subset of NuminaMathCoT using Qwen 2.5 3B Instruct as a judge. (the sharegpt-verified-cleaned subset from my dataset).
QLoRA Configuration:
- Rank: 32
- Rank Stabilization: Enabled
Optimization Settings:
- Batch Size: 8
- Gradient Accumulation Steps: 2 (Effective Batch Size: 16)
- Warm-Up Steps: 5
- Weight Decay: 0.01
Training Steps: 500 steps
Hardware Information: A100-80GB