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

Libraries

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

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

pipe = pipeline("text-generation", model="suayptalha/Qwen3-0.6B-Code-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-Code-Expert")
model = AutoModelForCausalLM.from_pretrained("suayptalha/Qwen3-0.6B-Code-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

vLLM

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

Use Docker

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

SGLang

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

Unsloth Studio new

How to use suayptalha/Qwen3-0.6B-Code-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-Code-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-Code-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-Code-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-Code-Expert",
    max_seq_length=2048,
)

Docker Model Runner
How to use suayptalha/Qwen3-0.6B-Code-Expert with Docker Model Runner:
```
docker model run hf.co/suayptalha/Qwen3-0.6B-Code-Expert
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-0.6B-Code-Expert

This project performs full fine-tuning on the Qwen3-0.6B language model to enhance its code reasoning and generation capabilities. Training was conducted exclusively on the nvidia/OpenCodeReasoning dataset, and the model was optimized using the bfloat16 (bf16) data type.

Training Procedure

Dataset Preparation
- nvidia/OpenCodeReasoning dataset was used.
- Each example consists of code snippets paired with detailed step-by-step reasoning in Chain-of-Thought (CoT) style.
Model Loading and Configuration
- Qwen3-0.6B base model weights were loaded via the unsloth library in bf16 precision.
- Full fine-tuning (full_finetuning=True) was applied to all layers for optimal adaptation to code reasoning.
Supervised Fine-Tuning
- Employed the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
- The model was trained to generate correct code solutions along with the corresponding reasoning chains.

Purpose and Outcome

The model’s capacity for understanding, reasoning about, and generating code was significantly improved through specialized, single-dataset training in bf16 precision.
Outputs include both intermediate reasoning steps and final code solutions, enabling transparent and interpretable code generation.