Instructions to use krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged with Transformers:

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

pipe = pipeline("text-generation", model="krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged")
model = AutoModelForCausalLM.from_pretrained("krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged")
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]:]))

PEFT
How to use krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged

SGLang

How to use krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged 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 "krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged" \
    --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": "krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged",
		"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 "krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged" \
        --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": "krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged 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 krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged 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 krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged",
    max_seq_length=2048,
)

Docker Model Runner
How to use krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged with Docker Model Runner:
```
docker model run hf.co/krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged
```

krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged

🚀 Harness the Power of Qwen-3 with Enhanced Reasoning and Chat! 🚀

This model is a carefully fine-tuned version of the incredible Qwen-3-8B using cutting-edge techniques with Unsloth and Parameter-Efficient Fine-Tuning (PEFT) via LoRA. It's designed to bring you the best of both worlds: the strong general capabilities of Qwen-3 with a significant boost in logical reasoning and engaging conversational skills.

We've taken the already powerful Qwen-3 and further sculpted it using a blend of the unsloth/OpenMathReasoning-mini (Chain-of-Thought split) for advanced problem-solving and the mlabonne/FineTome-100k dataset to ensure natural and fluent interactions.

🔥 Key Features:

Enhanced Reasoning: Excels at tasks requiring logical deduction and step-by-step thinking, thanks to fine-tuning on a dedicated reasoning dataset.
Improved Chat: Maintains and enhances the general conversational abilities of Qwen-3, making it great for interactive applications.
Efficient Fine-Tuning: Built using the incredibly efficient Unsloth library, resulting in faster training with less memory usage.
PEFT (LoRA) Inside: Leverages Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning, making it easier to adapt to specific tasks without full model retraining.
Ready to Use: Seamlessly integrates with the transformers library.

🛠️ How to Get Started:

Install the necessary libraries:

pip install transformers accelerate torch

Load and use the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "krishanwalia30/Qwen3-16bit-OpenMathReasoning-Finetuned-Merged"

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

messages = [
    {"role": "user", "content": "Explain the Pythagorean theorem in simple terms."},
    {"role": "assistant", "content": "Okay, here's a simple explanation:"},
    {"role": "user", "content": "Now, solve for the hypotenuse if a=3 and b=4."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.8, top_k=20, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

⚙️ Fine-tuning Details:

Base Model: Qwen-3-8B
Fine-tuning Framework: Unsloth
PEFT Strategy: LoRA
Training Datasets:
- unsloth/OpenMathReasoning-mini (COT split)
- mlabonne/FineTome-100k
Training Ratio: Approximately 30% reasoning data and 70% general chat data to balance capabilities.
Training Infrastructure: Google Colab with a T4 GPU.
Quantization during Training: Likely 4-bit quantization was employed during the fine-tuning process using Unsloth for memory efficiency. The final merged model is saved in 16-bit for broader compatibility.
Key Hyperparameters:
- per_device_train_batch_size: 2
- gradient_accumulation_steps: 4
- learning_rate: 2e-4
- max_steps: 30
- Optimizer: adamw_8bit
- Learning Rate Scheduler: linear
- Warmup Steps: 5
- Weight Decay: 0.01
- Seed: 3407

📊 Evaluation:

While rigorous quantitative evaluations are ongoing, initial assessments indicate a significant improvement in the model's ability to handle reasoning-based questions while maintaining strong general conversational skills. Further benchmarks and community feedback are welcome!

👨‍💻 Author:

[https://huggingface.co/krishanwalia30]

🔗 Learn More:

For a deeper dive into the fine-tuning process and the rationale behind the choices, check out the article: [https://medium.com/@krishanw30/b1a8f684c3f1].

🙏 Acknowledgements:

A big thank you to the brilliant teams at Qwen, Unsloth AI, and the creators of the OpenMathReasoning-mini and FineTome-100k datasets for making this project possible!