Update README.md

README.md

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 license: apache-2.0
 datasets:
 - sail/symbolic-instruction-tuning
----
+---
+
+# gemma-2B Fine-Tuning on SAIL/Symbolic-Instruction-Tuning
+
+This repository contains the `gemma-2B` model fine-tuned on the `sail/symbolic-instruction-tuning` dataset. The model is designed to interpret and execute symbolic instructions with improved accuracy and efficiency.
+
+## Overview
+
+The `gemma-2B` model, originally known for its robust language understanding capabilities, has been fine-tuned to enhance its performance on symbolic instruction data. This involves retraining the model on the `sail/symbolic-instruction-tuning` dataset, which comprises a diverse range of instructional data that tests a model's ability to follow abstract and complex directives.
+
+## Motivation
+
+The motivation behind fine-tuning `gemma-2B` on this particular dataset is to bridge the gap between language understanding and execution in a symbolic context. This has wide applications in areas such as code generation, automated reasoning, and more sophisticated AI instruction following.
+
+## Getting Started
+
+To use this model, you'll need to have an account on Hugging Face and the `transformers` library installed. You can install the library using pip:
+
+```bash
+pip install transformers
+```
+
+Once installed, you can use the following code to load and use the model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "your-huggingface-username/gemma-2B-fine-tuned"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Now you can use the model for inference
+input_text = "Your symbolic instruction here"
+input_ids = tokenizer.encode(input_text, return_tensors='pt')
+
+# Generate the output
+output = model.generate(input_ids)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Fine-Tuning Process
+
+The model was fine-tuned using the following process:
+
+- Preprocessing: The `sail/symbolic-instruction-tuning` dataset was preprocessed to conform with the input format required by `gemma-2B`.
+- Training: The model was fine-tuned using a custom training loop that monitors loss and evaluates on a held-out validation set.
+- Hyperparameters: The fine-tuning used specific hyperparameters, which you can find in the `training_script.py` file.
+- Evaluation: The fine-tuned model was evaluated against a benchmark to ensure that it meets our performance standards.