---
datasets:
- Programming-Language/codeagent-python
language:
- en
base_model:
- google/flan-t5-base
pipeline_tag: text-generation
library_name: transformers
license: apache-2.0
---

# flan-python-expert 🚀

This model is a fine-tuned version of [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) on the [`codeagent-python`](https://huggingface.co/datasets/Programming-Language/codeagent-python) dataset.

It is designed to generate Python code from natural language instructions. 

---

## 🧠 Model Details

- **Base Model:** FLAN-T5 Base
- **Fine-tuned on:** Python code dataset (`codeagent-python`)
- **Task:** Text-to-code generation
- **Language:** English
- **Framework:** 🤗 Transformers 
- **Library:** `adapter-transformers`

---

## 🏋️ Training

The model was trained using the following setup:

```python
from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="flan-python-expert",
    evaluation_strategy="epoch",
    learning_rate=2e-6,
    per_device_train_batch_size=1,
    per_device_eval_batch_size=1,
    num_train_epochs=1,
    weight_decay=0.01,
    save_total_limit=2,
    logging_steps=1,
    push_to_hub=False,
)

```


Trained for 1 epoch

Optimized for low-resource fine-tuning

Training performed using Hugging Face Trainer

## Example Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("MalikIbrar/flan-python-expert")
tokenizer = AutoTokenizer.from_pretrained("MalikIbrar/flan-python-expert")

input_text = "Write a Python function to check if a number is prime."
inputs = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(**inputs, max_length=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---