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Model: Mushari440/Qwen3-8B-SFT-v2
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- arabic
+- sft
+- qwen
+---
+
+# 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
+
+```python
+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))