---
license: apache-2.0
pipeline_tag: text-generation
tags:
- finetuned
inference: true
widget:
- messages:
  - role: user
    content: What is your favorite condiment?
---

# Fine-tuning Mistral-7B-v0.1 on Symbolic Instruction Tuning Dataset

This repository contains the fine-tuned version of the `mistralai/Mistral-7B-v0.1` model on the `sail/symbolic-instruction-tuning` dataset. The objective of this fine-tuning process is to specialize the pre-trained model for improved performance on tasks that require understanding and processing symbolic instructions.

## Model Description

`Mistral-7B-v0.1` is a transformer-based language model pre-trained on a diverse corpus of text. Our fine-tuning process aims to leverage this pre-trained model and further optimize it for the symbolic instruction tuning task provided by the `sail/symbolic-instruction-tuning` dataset.

## Dataset

The `sail/symbolic-instruction-tuning` dataset is designed to test a model's ability to comprehend and execute symbolic instructions. It consists of a series of tasks that require the model to manipulate symbolic inputs according to specific instructions.

## Fine-tuning Process

The fine-tuning process involves the following steps:

1. **Environment Setup**: Ensure that your environment has all the necessary dependencies installed, including `transformers` and `datasets` from Hugging Face.

2. **Data Preparation**: Load the `sail/symbolic-instruction-tuning` dataset using the `datasets` library and prepare it for the training process, including any necessary preprocessing steps.

3. **Model Initialization**: Load the pre-trained `mistralai/Mistral-7B-v0.1` model and prepare it for fine-tuning.

4. **Training**: Fine-tune the model on the prepared dataset using an appropriate training script. This involves setting hyperparameters, training loops, and logging.

5. **Evaluation**: Evaluate the fine-tuned model's performance on a validation set to ensure that it has learned the task effectively.

6. **Saving and Sharing**: Save the fine-tuned model and upload it to the Hugging Face model hub for easy sharing and reuse.

## Usage

The fine-tuned model can be loaded from the Hugging Face model hub using the `transformers` library as follows:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "rootsec1/mistal-7B-it-aipi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
inputs = tokenizer("Example input", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```