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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.
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