mistral-7B-it-aipi

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:

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))