toronto-restaurant-expert

Model Card for RajanChavada/toronto-restaurant-expert

A domain-specific, Toronto-focused restaurant, café, and matcha recommendation language model. Fine-tuned on 2,000+ crowdsourced prompts collected via TikTok review agents, giving cutting-edge advice for the local food scene.

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Model Details

Model Description

This is a fine-tuned version of Llama-2 (7B, quantized to 4-bit) enhanced with LoRA adapters. Training data features Toronto/Ontario food and drink recommendations, scraped and formatted as question/answer pairs. The model specializes in helping users find top restaurants, cafés, and especially matcha spots!

• Developed by: Rajan Chavada
• Funded by: Self-funded, student project
• Shared by: Rajan Chavada
• Model type: Causal Language Model (LLM)
• Language(s) (NLP): English
• License: MIT (or applicable Hugging Face base model license)
• Finetuned from model: unsloth/llama-2-7b-bnb-4bit

Model Sources

• Repository: https://huggingface.co/RajanChavada/toronto-restaurant-expert

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Uses

Direct Use

• Get hyper-local Toronto food, café, and matcha recommendations, driven by TikTok trends and crowdsourced reviews.

Downstream Use

• Integrate into chatbots or recommendation systems focused on Toronto food/drink discovery.
• Use as a template for further domain-specific fine-tuning.

Out-of-Scope Use

• General global restaurant advice.
• Safety, medical, or allergen advice.

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Bias, Risks, and Limitations

• Bias toward TikTok/social media trends.
• May over-represent popular venues; under-represent lesser-known spots.
• Not suitable for health or allergy-specific recommendations.

Recommendations

Always cross-check recommendations independently. Use for inspiration, not for medical, safety, or dietary-critical choices.

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How to Get Started