Instructions to use lucylq/qwen3_06B_lora_math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lucylq/qwen3_06B_lora_math with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-0.6B")
model = PeftModel.from_pretrained(base_model, "lucylq/qwen3_06B_lora_math")

Transformers

How to use lucylq/qwen3_06B_lora_math with Transformers:

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

pipe = pipeline("text-generation", model="lucylq/qwen3_06B_lora_math")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lucylq/qwen3_06B_lora_math", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lucylq/qwen3_06B_lora_math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lucylq/qwen3_06B_lora_math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucylq/qwen3_06B_lora_math",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lucylq/qwen3_06B_lora_math

SGLang

How to use lucylq/qwen3_06B_lora_math 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 "lucylq/qwen3_06B_lora_math" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucylq/qwen3_06B_lora_math",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "lucylq/qwen3_06B_lora_math" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucylq/qwen3_06B_lora_math",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use lucylq/qwen3_06B_lora_math 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 lucylq/qwen3_06B_lora_math 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 lucylq/qwen3_06B_lora_math to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lucylq/qwen3_06B_lora_math to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="lucylq/qwen3_06B_lora_math",
    max_seq_length=2048,
)

Docker Model Runner
How to use lucylq/qwen3_06B_lora_math with Docker Model Runner:
```
docker model run hf.co/lucylq/qwen3_06B_lora_math
```

Model Card for Model ID

Qwen0.6B trained on the MetaMathQA dataset using Unsloth. Used to test ExecuTorch LoRA capabilities.

Training Data

Dataset: https://huggingface.co/datasets/meta-math/MetaMathQA

Training Configuration

OUTPUT_DIR = "./outputs"
BATCH_SIZE = 2  # Smaller batch for longer sequences
GRADIENT_ACCUMULATION_STEPS = 8  # Effective batch = 16
LEARNING_RATE = 2e-4
NUM_EPOCHS = 1  # MetaMathQA is large, 1 epoch is often enough
WARMUP_RATIO = 0.03
LOGGING_STEPS = 25
SAVE_STEPS = 500
MAX_SAMPLES = 50000  # Limit samples for faster training (set None for full dataset)