metadata
license: mit
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
license: llama3 tags: - hotel-review-triage - hospitality - structured-output - json-generation - complaint-routing - llama - sft - lora - transformers - bitsandbytes model_name: llama32-hotel-review-triage base_model: meta-llama/Llama-3.2-1B-Instruct datasets: - public-hotel-reviews language: en pipeline_tag: text-generation inference: true
🏨 LLaMA‑3.2 Hotel Review Triage Model
Structured JSON Extraction for Hospitality Operations
🔹 Model Overview
Model Name: llama32-hotel-review-triage
Base Model: meta-llama/Llama-3.2-1B-Instruct
Domain: Hospitality / Hotel Operations
Task: Hotel review triage → structured JSON output
Fine‑tuning Method: SFT + LoRA (merged)
Language: English
Validation Perplexity: 3.02
This model converts unstructured hotel guest reviews into clean, machine‑readable JSON, enabling automated complaint routing, severity detection, and service analytics.
This model is designed to bridge the gap between unstructured guest feedback and actionable hotel operations by converting free-text reviews into structured JSON. While raw sentiment alone offers limited operational value, structured outputs enable hotels to systematically understand what went wrong, how severe the issue is, and which department is responsible. By transforming subjective guest language into consistent machine-readable signals, the model allows reviews to be automatically routed, aggregated, and analyzed at scale. This structure makes it possible to power downstream systems such as issue-tracking workflows, operational dashboards, AI hotel agents, and real-time escalation logic. In practice, the model enables hotels to move from passive review monitoring to proactive service recovery, where guest feedback directly informs decisions, prioritization, and automated responses across housekeeping, engineering, and front-office teams.
🎯 Intended Use
Primary Use Cases
- Hotel guest review analysis
- Complaint triage & categorization
- Department routing (housekeeping, engineering, front desk)
- Severity & priority detection
- Input preprocessing for dashboards & ticketing systems
Typical Applications
- Review ingestion pipelines (Google Reviews, TripAdvisor, surveys)
- Hospitality analytics platforms
- AI‑powered hotel service agents
- Internal customer experience tools
🧾 Input & Output Format
Input
Plain‑text hotel guest review.
Output
- Strict JSON
- No explanations
- No natural language outside JSON
Example Output
{
"issues": [
{
"category": "maintenance",
"description": "Air conditioning stopped working during the night",
"severity": "high",
"department": "engineering"
},
{
"category": "service",
"description": "Front desk response was delayed",
"severity": "medium",
"department": "front_desk"
}
],
"overall_sentiment": "negative",
"priority": "high"
}
🏗 Model Architecture
- Architecture: Decoder‑only causal language model
- Parameters: ~1B
- Backbone: LLaMA 3.2
- Tokenizer: LLaMA 3.2 tokenizer (chat template preserved)
- Precision: BF16 / FP16
LoRA Fine‑Tuning Details
Adapters applied to:
- Attention projections
- MLP layers
Adapters merged post‑training for standalone deployment.
📊 Training Details
Dataset
- Source: Public hotel review datasets
- Domain: Real‑world guest feedback
- Language: English
- Training Examples: 120,000
- Validation: Held‑out split
Preprocessing
- Removed hotel names & dates
- Converted reviews into instruction‑style chat format
- Supervised training toward structured JSON outputs
Training Configuration
- Epochs: 3
- Max Sequence Length: 512
- Optimizer: AdamW
- Hardware: NVIDIA A100 GPU
- Training Time: ~10 hours
- Strategy: QLoRA‑style training, merged after completion
📈 Evaluation
Quantitative
- Validation Perplexity: 3.02
Qualitative
Evaluated on:
- Multi‑issue complaints
- Mixed sentiment reviews
- Policy‑related feedback
- Hygiene & safety‑critical cases
Observed Strengths
- Consistent JSON formatting
- Accurate department routing
- Appropriate severity assignment
- Robust handling of noisy real‑world text
⚠️ Limitations
- Trained only on English hotel reviews
- Not suitable for legal, medical, or safety‑critical decisions
- JSON schema is prompt‑dependent
- May struggle with:
- Very short or sarcastic reviews
- Highly ambiguous feedback
- Non‑hotel domains (airlines, cruises, etc.)
Use as a decision‑support tool, not a final authority.
⚖️ Ethical Considerations
- May reflect biases present in user‑generated reviews
- Should not be used for profiling individuals
- Avoid passing personal or sensitive data into the model
🚀 How to Use
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "Amey9766/llama32-hotel-review-triage"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto"
)
prompt = tokenizer.apply_chat_template(
[
{"role": "system", "content": "You are a hospitality review triage assistant. Output ONLY valid JSON."},
{"role": "user", "content": "The room was dirty and the AC didn’t work."}
],
tokenize=False
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(output[0], skip_special_tokens=True))
📚 Citation
@misc{tillu2026llama32hoteltriage,
title = {LLaMA-3.2 Hotel Review Triage Model},
author = {Amey Tillu},
year = {2026},
howpublished = {\url{https://huggingface.co/Amey9766/llama32-hotel-review-triage}},
note = {Fine-tuned on hospitality reviews for structured JSON triage}
}
📜 License
This model inherits the license of its base model:
LLaMA 3.2 License (Meta)
Please ensure compliance when using or redistributing this model.
🙏 Acknowledgements
- Meta AI for the LLaMA‑3.2 base model
- Hugging Face ecosystem (Transformers, PEFT, TRL)
- Public hospitality review datasets used for training
⭐ When to Use This Model
Use this model when you need:
- Reliable structured outputs
- Fast review triage
- Integration into hotel operations pipelines
- A foundation for AI hospitality agents