Instructions to use suayptalha/Qwen3-0.6B-Math-Expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suayptalha/Qwen3-0.6B-Math-Expert with Transformers:

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

pipe = pipeline("text-generation", model="suayptalha/Qwen3-0.6B-Math-Expert")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("suayptalha/Qwen3-0.6B-Math-Expert")
model = AutoModelForCausalLM.from_pretrained("suayptalha/Qwen3-0.6B-Math-Expert")
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 suayptalha/Qwen3-0.6B-Math-Expert with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suayptalha/Qwen3-0.6B-Math-Expert"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suayptalha/Qwen3-0.6B-Math-Expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/suayptalha/Qwen3-0.6B-Math-Expert

SGLang

How to use suayptalha/Qwen3-0.6B-Math-Expert 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 "suayptalha/Qwen3-0.6B-Math-Expert" \
    --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": "suayptalha/Qwen3-0.6B-Math-Expert",
		"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 "suayptalha/Qwen3-0.6B-Math-Expert" \
        --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": "suayptalha/Qwen3-0.6B-Math-Expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use suayptalha/Qwen3-0.6B-Math-Expert 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 suayptalha/Qwen3-0.6B-Math-Expert 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 suayptalha/Qwen3-0.6B-Math-Expert to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for suayptalha/Qwen3-0.6B-Math-Expert to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="suayptalha/Qwen3-0.6B-Math-Expert",
    max_seq_length=2048,
)

Docker Model Runner
How to use suayptalha/Qwen3-0.6B-Math-Expert with Docker Model Runner:
```
docker model run hf.co/suayptalha/Qwen3-0.6B-Math-Expert
```

suayptalha commited on May 9, 2025

Commit

559ef38

verified ·

1 Parent(s): d396a45

Update README.md

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README.md +31 -1

README.md CHANGED Viewed

@@ -12,4 +12,34 @@ base_model:
 - Qwen/Qwen3-0.6B
 pipeline_tag: text-generation
 library_name: transformers
----

 - Qwen/Qwen3-0.6B
 pipeline_tag: text-generation
 library_name: transformers
+---
+# Qwen3-0.6B-Math-Expert
+This project performs full fine-tuning on the **Qwen3-0.6B** language model to enhance its mathematical problem-solving and reasoning capabilities. Training was conducted exclusively on the `OpenMathReasoning-mini` dataset, and the model was optimized using the bfloat16 (bf16) data type.
+## Training Procedure
+1. **Dataset Preparation**
+   * The `unsloth/OpenMathReasoning-mini` dataset was used.
+   * Each example was formatted in Chain-of-Thought (CoT) style, pairing math problems with step-by-step intermediate reasoning.
+2. **Model Loading and Configuration**
+   * Qwen3 base model weights were loaded via the `unsloth` library in bf16 precision.
+   * All layers were updated (`full_finetuning=True`) to adapt the model for mathematical reasoning.
+3. **Supervised Fine-Tuning**
+   * Leveraged the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
+   * The model was trained to generate both correct answers and corresponding reasoning chains.
+## Purpose and Outcome
+* The model’s reasoning capacity for math problems was significantly improved through single-dataset, full fine-tuning in bf16 precision.
+* Outputs include both intermediate reasoning steps and final solutions, providing transparent and interpretable results.
+## License
+This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details.