---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets:
- iamtarun/python_code_instructions_18k_alpaca
language:
- ar
- en
pipeline_tag: text-generation
tags:
- llama-factory
- lora
- qwen2
- python
- arabic
- code
- instruction-tuning
- fine-tuned
---

# 🐍 Python Assistant (Arabic)

A fine-tuned version of **Qwen2.5-1.5B-Instruct** that answers Python programming questions in **Arabic**, with structured JSON output. Fine-tuned using LoRA via LLaMA-Factory.

---

## Model Details

- **Developed by:** jana-ashraf-ai  
- **Base Model:** [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)  
- **Model type:** Causal Language Model (text-generation)  
- **Language(s):** Arabic (answers) + English (questions)  
- **License:** Apache 2.0  
- **Fine-tuning method:** QLoRA (LoRA rank=32) via LLaMA-Factory  

---

## What does this model do?

Given a Python programming question in English, the model returns a structured JSON answer **in Arabic**, explaining the solution step by step.

---

## How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "jana-ashraf-ai/python-assistant"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

system_prompt = """You are a Python expert assistant.
Answer the user's Python question in Arabic following the Output Schema.
Do not add any introduction or conclusion."""

question = "How do I reverse a list in Python?"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": question}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## Training Details

| Parameter | Value |
|-----------|-------|
| Base model | Qwen2.5-1.5B-Instruct |
| Fine-tuning method | LoRA (QLoRA) |
| LoRA rank | 32 |
| LoRA target | all |
| Training samples | 1,000 |
| Epochs | 3 |
| Learning rate | 1e-4 |
| LR scheduler | cosine |
| Warmup ratio | 0.1 |
| Batch size | 1 (grad accum = 8) |
| Precision | fp16 |
| Quantization | 4-bit (nf4) |
| Framework | LLaMA-Factory |
| Hardware | Google Colab T4 GPU |

---

## Training Data

Fine-tuned on a curated subset (1,000 samples) from [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca).

The answers were annotated and structured using GPT to produce Arabic explanations in a JSON schema format.

**Train / Val split:** 90% / 10%

---

## Limitations

- The model is optimized for Python questions only.
- Answers are in Arabic — not suitable for English-only use cases.
- Small model size (1.5B) may struggle with very complex programming problems.
- Output quality depends on the question being clear and specific.