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| Benchmark | Score |
|---|---|
| Arabic Code Explanation | 100% (5/5) |
| MBPP Syntax Rate | 92.3% |
| MBPP Execution Rate | 82.3% |
| Multi-Language (Python / JS / SQL) | 3/3 |
| Inference Speed | 25.9 tok/s |
1from unsloth import FastLanguageModel
2
3model, tokenizer = FastLanguageModel.from_pretrained(
4 model_name="rahimdzx/AraCode-7B-LoRA",
5 max_seq_length=2048,
6 load_in_4bit=True,
7)
8FastLanguageModel.for_inference(model)
9
10prompt = "اشرح الكود التالي بالعربية:\ndef fibonacci(n):\n if n <= 1: return n\n return fibonacci(n-1) + fibonacci(n-2)"
11
12inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
13outputs = model.generate(**inputs, max_new_tokens=300)
14print(tokenizer.decode(outputs[0], skip_special_tokens=True))| Format | Repo | Size | Use Case |
|---|---|---|---|
| GGUF Q4_K_M | AraCode-7B-GGUF | 4.68 GB | Local inference, Ollama, llama.cpp |
| LoRA Adapter | AraCode-7B-LoRA | 162 MB | Fine-tuning, research, Unsloth |