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