Amey9766/llama-3.2-3b-instruct-revenue-decision-merged

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This model is a fine-tuned and fully merged variant of Meta LLaMA-3.2-1B-Instruct, adapted for hospitality revenue management and decision-support tasks. Fine-tuning was performed using supervised fine-tuning (SFT) with LoRA adapters to efficiently specialize the base model while keeping training costs manageable. The training data consisted of domain-specific instruction–response pairs covering pricing strategy, demand forecasting reasoning, inventory and capacity trade-offs, seasonality impacts, and revenue optimization scenarios commonly encountered in hospitality operations. Training was conducted using the Hugging Face Trainer framework with mixed-precision (FP16), gradient accumulation, and an 8-bit AdamW optimizer to support memory-efficient training. After convergence, the LoRA adapters were merged into the base model weights, producing a standalone model that no longer requires external adapters and can be deployed directly for inference in production or Hugging Face Spaces. license: llama3 base_model: meta-llama/Llama-3.2-1B-Instruct library_name: transformers pipeline_tag: text-generation tags: - llama - llama-3 - instruction-tuned - hospitality - revenue-management - decision-support - merged - safetensors model_type: causal-lm language: - en

LLaMA-3.2-1B-Instruct-Revenue-Decision (Merged)

Model Summary

LLaMA-3.2-3B-Instruct-Revenue-Decision-Merged is a domain-adapted large language model designed to assist with hotel revenue management and decision-making tasks.
The model is based on Meta LLaMA-3.2-1B-Instruct and was fine-tuned using supervised fine-tuning (SFT) on hospitality-specific data, followed by LoRA adaptation and a full weight merge into the base model.

This repository contains the fully merged model weights, meaning no LoRA adapters are required at inference time.

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Intended Use

This model is intended for decision support and analytical reasoning in hospitality and travel contexts, including:

• Hotel pricing and rate strategy recommendations
• Occupancy and demand-based decision reasoning
• Revenue optimization scenarios
• Business and operational trade-off analysis
• Scenario-based “what should I do?” reasoning

It is suitable for:

• Research
• Prototyping
• AI agents
• Internal decision-support tools

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Not Intended For

• Medical, legal, or financial advice
• Fully autonomous decision-making without human oversight
• High-risk or safety-critical systems

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Training Details

• Base Model: meta-llama/Llama-3.2-1B-Instruct
• Fine-Tuning Method: Supervised Fine-Tuning (SFT)
• Adaptation Technique: LoRA
• LoRA Rank (r): 16
• LoRA Alpha: 32
• Final Model: Fully merged LoRA + base weights
• Framework: Hugging Face Transformers + PEFT

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Model Architecture

• Decoder-only causal language model
• Transformer architecture
• Chat-optimized instruction format
• Supports chat templates via chat_template.jinja

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Files Included

This repository includes all files required for inference and deployment:

• model-00001-of-00002.safetensors
• model-00002-of-00002.safetensors
• model.safetensors.index.json
• config.json
• generation_config.json
• tokenizer.json
• tokenizer_config.json
• special_tokens_map.json
• chat_template.jinja

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Base model citation-

@misc{llama3, title = {The LLaMA 3 Model Family}, author = {Meta AI}, year = {2024}, howpublished = {\url{https://ai.meta.com/llama/}} }

Tillu, A. (2026). LLaMA-3.2-3B-Instruct Revenue Decision Model (Merged). Hugging Face. https://huggingface.co/Amey9766/llama-3.2-3b-instruct-revenue-decision-merged

Author- Amey Tillu Hospitality Data Analyst AI/ML Hobbyist

Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Amey9766/llama-3.2-3b-instruct-revenue-decision-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "Demand is increasing but weekday occupancy is low. What pricing strategy should a hotel consider?"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        temperature=0.7
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))