Update README.md

README.md

@@ -229,4 +229,181 @@ Meta AI for the LLaMA 3.2 base model
 
 Hugging Face ecosystem (Transformers, PEFT, TRL)
 
-Public hotel review datasets used for training
+Public hotel review datasets used for training
+
+If you want next-level polish, I can:
+
+optimize tags + metadata for Hugging Face search
+
+add visual badges (perplexity, domain, task)
+
+write a short launch post for LinkedIn / X
+
+Just tell me 👍
+
+it is still looking weird
+
+You’re right — what you have works, but Hugging Face renders Markdown very literally.
+Right now your card looks like a wall of text because it’s missing clear hierarchy, spacing, bullets, and visual anchors.
+
+Below is a clean, well-structured, Hugging Face–optimized model card that you can copy-paste directly into README.md.
+It will render nicely with bold headings, bullets, code blocks, and emojis (used sparingly).
+
+🚀 LLaMA-3.2 Hotel Review Triage Model
+
+Model ID: Amey9766/llama32-hotel-review-triage
+Base Model: meta-llama/Llama-3.2-1B-Instruct
+Author: Amey Tillu
+Task: Hospitality Review Triage → Structured JSON Output
+Perplexity (validation): 3.02
+
+🧠 What This Model Does
+
+This model is fine-tuned to analyze hotel guest reviews and convert unstructured feedback into clean, machine-readable JSON suitable for:
+
+Complaint routing
+
+Guest experience analytics
+
+Operational alerting
+
+AI hotel agents
+
+It is designed to behave as a deterministic triage system, not a conversational chatbot.
+
+📌 Core Capabilities
+
+Identifies complaint category (e.g. cleanliness, staff, facilities)
+
+Estimates severity level (low / medium / high)
+
+Infers department ownership (housekeeping, front desk, maintenance)
+
+Produces strict, valid JSON output
+
+Handles both negative complaints and positive feedback
+
+🏨 Example Use Case
+
+Input
+
+The room was dirty and the AC didn’t work.
+
+
+Output
+
+{
+  "category": "room_maintenance",
+  "severity": "high",
+  "department": "engineering",
+  "sentiment": "negative",
+  "summary": "Guest reports cleanliness issues and malfunctioning air conditioning."
+}
+
+🧪 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))
+
+📊 Training Details
+
+Dataset: Public hotel review datasets (Hugging Face)
+
+Total examples used: ~120,000
+
+Train / Validation split: 90% / 10%
+
+Sequence length: 512 tokens
+
+Fine-tuning method: LoRA (PEFT)
+
+Training hardware: NVIDIA A100
+
+Training time: ~10 hours
+
+Epochs: 3
+
+📈 Evaluation
+
+Validation Perplexity: 3.02
+
+Stable loss convergence
+
+No observed mode collapse
+
+Minor variance expected due to diverse review lengths and tone
+
+⚠️ Limitations
+
+Not trained on live hotel policies or property-specific rules
+
+Does not replace human judgment for legal or compensation decisions
+
+Assumes English-language reviews
+
+Output schema is opinionated and may require adaptation
+
+🔐 License
+
+This model inherits the license of its base model:
+
+LLaMA 3.2 License (Meta)
+
+Please ensure compliance with the base model’s license when using or redistributing this model.
+
+📚 Citation
+
+If you use this model in research or production systems, please cite:
+
+@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}
+}
+
+🙏 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