115 lines
3.0 KiB
Markdown
115 lines
3.0 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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datasets:
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- iamtarun/python_code_instructions_18k_alpaca
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language:
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- ar
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- en
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pipeline_tag: text-generation
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tags:
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- llama-factory
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- lora
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- qwen2
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- python
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- arabic
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- code
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- instruction-tuning
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- fine-tuned
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---
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# 🐍 Python Assistant (Arabic)
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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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---
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## Model Details
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- **Developed by:** jana-ashraf-ai
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- **Base Model:** [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
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- **Model type:** Causal Language Model (text-generation)
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- **Language(s):** Arabic (answers) + English (questions)
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- **License:** Apache 2.0
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- **Fine-tuning method:** QLoRA (LoRA rank=32) via LLaMA-Factory
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---
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## What does this model do?
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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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---
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## How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "jana-ashraf-ai/python-assistant"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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system_prompt = """You are a Python expert assistant.
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Answer the user's Python question in Arabic following the Output Schema.
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Do not add any introduction or conclusion."""
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question = "How do I reverse a list in Python?"
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": question}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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## Training Details
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| Parameter | Value |
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|-----------|-------|
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| Base model | Qwen2.5-1.5B-Instruct |
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| Fine-tuning method | LoRA (QLoRA) |
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| LoRA rank | 32 |
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| LoRA target | all |
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| Training samples | 1,000 |
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| Epochs | 3 |
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| Learning rate | 1e-4 |
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| LR scheduler | cosine |
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| Warmup ratio | 0.1 |
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| Batch size | 1 (grad accum = 8) |
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| Precision | fp16 |
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| Quantization | 4-bit (nf4) |
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| Framework | LLaMA-Factory |
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| Hardware | Google Colab T4 GPU |
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---
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## Training Data
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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).
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The answers were annotated and structured using GPT to produce Arabic explanations in a JSON schema format.
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**Train / Val split:** 90% / 10%
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---
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## Limitations
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- The model is optimized for Python questions only.
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- Answers are in Arabic — not suitable for English-only use cases.
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- Small model size (1.5B) may struggle with very complex programming problems.
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- Output quality depends on the question being clear and specific. |