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

Libraries

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

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

pipe = pipeline("text-generation", model="suayptalha/Qwen3-0.6B-IF-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-IF-Expert")
model = AutoModelForCausalLM.from_pretrained("suayptalha/Qwen3-0.6B-IF-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-IF-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-IF-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-IF-Expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Unsloth Studio

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

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

Qwen3-0.6B-IF-Expert

This project performs full fine-tuning on the Qwen3-0.6B language model to enhance its instruction-following and reasoning capabilities. Training was conducted on the patrickfleith/instruction-freak-reasoning dataset using bfloat16 (bf16) precision for efficient optimization.

Training Procedure

Dataset Preparation
- The patrickfleith/instruction-freak-reasoning dataset was used.
- Each example contains a complex instruction paired with an in-depth reasoning-based response.
- Prompts were structured to encourage chain-of-thought style outputs when applicable.
Model Loading and Configuration
- Qwen3 base model weights were loaded via the unsloth library in bf16 precision.
- All model layers were fully updated (full_finetuning=True) to effectively adapt the model to instruction understanding and stepwise response generation.
Supervised Fine-Tuning
- Fine-tuning was conducted using the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
- The model was trained to follow detailed instructions, reason logically, and generate structured responses.

Purpose and Outcome

The model’s ability to follow complex instructions and explain its reasoning process has been significantly enhanced.
It generates both coherent reasoning steps and conclusive answers, improving transparency and usability for instruction-based tasks.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Support

Downloads last month: 9

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for suayptalha/Qwen3-0.6B-IF-Expert

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

(962)

this model

Merges

2 models

suayptalha
/

Qwen3-0.6B-IF-Expert