library_name, tags
| library_name | tags | |||
|---|---|---|---|---|
| transformers |
|
Qwen3-8B-SFT
Model Details
- Developed by: Mushari Alothman
- Model type: Causal Language Model
- Language(s): Arabic, English
- License: Apache 2.0
- Finetuned from: Qwen3-8B-Base
This is a supervised fine-tuned (SFT) Qwen3-8B model optimized for accurate, clean supervision across Arabic and English tasks.
Intended Uses
Direct Use
- Arabic & English MCQ answering
- Context-based QA / RAG
- General instruction following
Out-of-Scope Use
- Safety-critical or real-time decision making
- Generating factual guarantees without verification
Training Summary
- Training type: Supervised Fine-Tuning (SFT)
- Precision: bf16 mixed precision
- Data: Curated Arabic & English datasets including:
- MCQ
- QA / RAG / context understanding
- General instruction data
How to Use
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Mushari440/Qwen3-8B-SFT-v2")
model = AutoModelForCausalLM.from_pretrained("Mushari440/Qwen3-8B-SFT-v2")
inputs = tokenizer("سؤال: ما عاصمة السعودية؟", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))