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----
 library_name: transformers
 tags:
 - llama-factory
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-- **Model type:** [More Information Needed]
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-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
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-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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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.