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Model: jana-ashraf-ai/python-assistant
Source: Original Platform

README.md

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+---
+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.