The Go-Gourmet model is a fine-tuned version of the mistralai/Mistral-7B-Instruct-v0.2 base model with 32k context window, specifically trained to generate structured restaurant cards based on an input of a restaurant name and location. The model has been fine-tuned using a custom dataset of restaurant information to capture relevant details such as cuisine, opening times, location, rating, average price, best dishes, pre-booking requirements, dress code, and website.

Model Details

Base Model: mistralai/Mistral-7B-Instruct-v0.2.
Fine-Tuning Dataset: Custom dataset of restaurant information Sadiah/Go-Gourmet.
Fine-Tuning Approach: QLoRA and SFT Trainer.
Model Size: The model retains the same size and architecture as the original Mistral base model.

Intended Use

The Go-Gourmet Fine-Tuned Mistral Model is designed to generate structured restaurant cards based on an input of a restaurant name and location. It can be used for various purposes, such as:

Generating informative restaurant cards for food and travel applications
Providing quick and structured information about restaurants to users
Enhancing natural language processing applications related to the food and hospitality industry

Limitations and Considerations

The model's outputs are generated based on patterns and characteristics learned from the fine-tuning dataset. While it aims to provide accurate and relevant information, the generated restaurant cards may not always be perfect or up-to-date.
The model relies on the quality and comprehensiveness of the fine-tuning dataset. If certain details or categories are missing from the dataset, the model may not be able to generate them accurately.
The generated restaurant cards should be used as a starting point and should be verified with official sources or the restaurants themselves for the most accurate and current information.

Inference Code

To test and interact with the Go-Gourmet Fine-Tuned Mistral Model, you can use the following inference code:

python

# Load the fine-tuned model from hugging face
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer_new = AutoTokenizer.from_pretrained("Sadiah/Go-Gourmet")
model_new = AutoModelForCausalLM.from_pretrained(
    "Sadiah/Go-Gourmet", 
    trust_remote_code=True, 
    torch_dtype=torch.float16,
    device_map= {"": 0},
)

input_text = '''[INST]Olives, Delhi [/INST]''' #Define instruction
input_ids = tokenizer_new(input_text, return_tensors="pt") #Tokenize instruction
input_ids = input_ids.to("cuda") #Move instruction to GPU
outputs = model_new.generate(**input_ids, max_length=300, num_return_sequences=1, temperature=0) #Generate response
generated_text = tokenizer_new.decode(outputs[0]) #Decode generated response

# Find the index of the closing instruction tag and remove the instruction
instruction_end_index = generated_text.find("[/INST]")
if instruction_end_index != -1:
    generated_text = generated_text[instruction_end_index + len("[/INST]"):].strip()

# Find the index of "Website:" and the end of the website address
website_start_index = generated_text.find("Website:")
if website_start_index != -1:
    website_end_index = generated_text.find("\n", website_start_index)
    if website_end_index == -1:
        website_end_index = len(generated_text)
    truncated_text = generated_text[:website_end_index]
    print(truncated_text.strip())
else:
    print(generated_text)

Name: Olives, Delhi 
Cuisine: Mediterranean 
Opening Times: Mon-Sun: 12:30pm-3:30pm, 7pm-11:30pm 
Location: 1, Kalka Das Marg, New Delhi, Delhi 110001, India (28.7031, 77.1123) 
Rating: 4.3 (Source: Zomato) 
Average Price Per Person: Moderate 
Three Best Dishes: 
1. Grilled Halloumi Cheese: A popular Mediterranean cheese, grilled to perfection.
2. Falafel Platter: A selection of crispy, flavorful falafel balls served with hummus and pita bread.
3. Lamb Shank Tagine: Slow-cooked lamb shank in a rich, aromatic sauce.
Pre-Booking Needed: Recommended, especially for dinner 
Dress Code: Casual 
Website: http://www.olivesdelhi.com/

This code snippet allows you to provide an input of a restaurant name and location and generate a structured restaurant card using the Go-Gourmet Fine-Tuned Mistral Model.

Contact and Feedback

If you have any questions, feedback, or concerns regarding the Go-Gourmet Fine-Tuned Mistral Model, please contact me at https://www.sadiahzahoor.com/contact .