You can deploy this model and start earning money today!

Amey9766/
llama32B-hospitality-review-triage

Name: llama32B-hospitality-review-triage
Availability: InStock

safetensors

llama

meta-llama/Llama-3.2-3B-Instruct

Views

No views yet

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

json

{
  "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

python

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

Amey9766/llama32B-hospitality-review-triage