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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))
```
--- |