Instructions to use RajanChavada/toronto-restaurant-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RajanChavada/toronto-restaurant-expert with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-2-7b-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "RajanChavada/toronto-restaurant-expert")

Transformers

How to use RajanChavada/toronto-restaurant-expert with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RajanChavada/toronto-restaurant-expert")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("RajanChavada/toronto-restaurant-expert", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RajanChavada/toronto-restaurant-expert with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RajanChavada/toronto-restaurant-expert"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RajanChavada/toronto-restaurant-expert",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RajanChavada/toronto-restaurant-expert

SGLang

How to use RajanChavada/toronto-restaurant-expert with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RajanChavada/toronto-restaurant-expert" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RajanChavada/toronto-restaurant-expert",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RajanChavada/toronto-restaurant-expert" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RajanChavada/toronto-restaurant-expert",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use RajanChavada/toronto-restaurant-expert with Docker Model Runner:
```
docker model run hf.co/RajanChavada/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.

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

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.

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.

How to Get Started

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