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--- |
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license: apache-2.0 |
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datasets: |
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- sail/symbolic-instruction-tuning |
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--- |
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# gemma-2B Fine-Tuning on SAIL/Symbolic-Instruction-Tuning |
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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. |
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## Overview |
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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. |
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## Motivation |
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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. |
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## Getting Started |
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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: |
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```bash |
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pip install transformers |
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``` |
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Once installed, you can use the following code to load and use the model: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "your-huggingface-username/gemma-2B-fine-tuned" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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# Now you can use the model for inference |
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input_text = "Your symbolic instruction here" |
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input_ids = tokenizer.encode(input_text, return_tensors='pt') |
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# Generate the output |
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output = model.generate(input_ids) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
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## Fine-Tuning Process |
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The model was fine-tuned using the following process: |
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- Preprocessing: The `sail/symbolic-instruction-tuning` dataset was preprocessed to conform with the input format required by `gemma-2B`. |
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- Training: The model was fine-tuned using a custom training loop that monitors loss and evaluates on a held-out validation set. |
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- Hyperparameters: The fine-tuning used specific hyperparameters, which you can find in the `training_script.py` file. |
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- Evaluation: The fine-tuned model was evaluated against a benchmark to ensure that it meets our performance standards. |
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