This repository contains a fine-tuned version of the Mistral 7B Instruct v0.3 model, specialized for generating high-quality meal reviews. The model was created by merging a LoRA adapter (available at Oliver1703dk/meal_review_fine_tuned_adapter_bigger) with the base Mistral 7B model, using the Food.com dataset for fine-tuning.

Model Details

Base Model: mistralai/Mistral-7B-Instruct-v0.3
Fine-Tuning Method: LoRA (Low-Rank Adaptation), merged with the base model
Task: Text generation for meal reviews
Training Data: The Food.com Recipes and User Interactions dataset, specifically the user review text. The dataset contains over 700,000 recipe reviews, which were preprocessed to focus on review generation.
Training Steps: 12,714 steps

Usage

The model can be used directly for inference with the library. Below is an example of how to load and use the model.

Installation

pip install transformers torch

Example Code

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "Oliver1703dk/meal_review_merged_mistral_finetuned_bigger",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Oliver1703dk/meal_review_merged_mistral_finetuned_bigger")

# Inference
prompt = "Write a review for a delicious Italian meal."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or issues, please open an issue in this repository or contact Oliver1703dk.

Generated on April 29, 2025