jana-ashraf-ai/python-assistant

🐍 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.

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Model Details

• Developed by: jana-ashraf-ai
• Base Model: 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

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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.

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How to Use

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))

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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

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Training Data

Fine-tuned on a curated subset (1,000 samples) from 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%

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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.