Update README.md

README.md

@@ -7,4 +7,69 @@ base_model:
 - google/flan-t5-base
 pipeline_tag: text-generation
 library_name: adapter-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))
+```
+
 ---