Instructions to use omuralcin/qwen3-hadis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use omuralcin/qwen3-hadis 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 omuralcin/qwen3-hadis 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 omuralcin/qwen3-hadis to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for omuralcin/qwen3-hadis to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="omuralcin/qwen3-hadis", max_seq_length=2048, )
Qwen3-4B Hadis Fine-tune
Qwen3-4B modelinin hadis verileriyle fine-tune edilmiş Türkçe/Arapça versiyonu.
Model Hakkında
- Base model: Qwen3-4B
- Eğitim verisi: 138.789 hadis çifti
- Eğitim yöntemi: QLoRA (4-bit)
- Eğitim süresi: ~17 dakika (L40S)
Kullanım
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="omuralcin/qwen3-hadis",
load_in_4bit=True,
)
GGUF Versiyonu
Notlar
- Loss: 0.33 (stabil)
- Packing aktif, verimli eğitim
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