MightyOctopus/pricer-merged-model-A-v1

Model Card for Model ID

A merged LLaMA 3.1 8B checkpoint specialized for numeric price prediction from product text. This model was created by merging LLaMA 3.1 8B with a LoRA adapter (Pricer LoRA v1) and is intended to serve as a base model for further LoRA fine-tuning.

Model Details

Model Description

pricer-merged-model-A-v1 is a transformer-based causal language model optimized for estimating approximate consumer product prices from textual metadata such as title, description, and category. It represents a merged checkpoint (base model + LoRA), not an adapter-only model.

• Developed by: MyungHwan Hong (MightyOctopus)
• Funded by: Self-funded / independent research
• Shared by: MyungHwan Hong
• Model type: Causal Language Model (Text-to-Number / Numeric Prediction)
• Language(s) (NLP): English
• License: MIT
• Finetuned from model: meta-llama/Llama-3.1-8B

Model Sources [optional]

• Repository: https://huggingface.co/MightyOctopus/pricer-merged-model-A-v1
• Paper: N/A
• Demo: N/A

Uses

Direct Use

• Base checkpoint for price-prediction inference

• Base model for further LoRA fine-tuning

• Research on LLM-based numeric regression

Downstream Use [optional]

• Domain-specific pricing models

• Comparative studies vs classical ML regressors

• Educational experiments on LoRA merging strategies

Out-of-Scope Use

• Real-time or production pricing systems

• Financial decision-making

• Legal, medical, or safety-critical applications

• Use as an authoritative price source

Bias, Risks, and Limitations

• Predictions are approximate, not exact

• Performance depends on similarity to training data distribution

• May hallucinate prices for unfamiliar products

• Reflects historical and dataset-specific price biases

• Not robust to rapid market price changes

Recommendations

• Treat outputs as estimates, not ground truth

• Validate predictions with real pricing data

• Avoid high-stakes or commercial deployment

• Be aware of temporal and dataset bias

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "MightyOctopus/pricer-merged-model-A-v1"
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = """Product:
Title: Stainless Steel Electric Kettle 1.7L
Category: Home & Kitchen
Description: Fast boiling electric kettle with auto shut-off.

Price is $"""

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

with torch.no_grad():
    outputs = model.generate(inputs.input_ids, max_new_tokens=10)

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

Training Details

Training Data

• Amazon product metadata

• Fields: title, description, category, ground-truth price

• Prices represented as structured text outputs

Training Procedure

This model was created by merging a LoRA adapter (Pricer LoRA v1) into LLaMA 3.1 8B. No additional training was performed after merging.

Training Hyperparameters

• Training regime: bfloat16 mixed precision (inherited from LoRA training)

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

This merged checkpoint was not evaluated independently. Evaluation is performed on downstream fine-tuned adapters (e.g., pricer-lora-ft-v3).

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: NVIDIA GPU (e.g. T4, A100)
• Hours used: ~20 hours (LoRA v1 training)
• Cloud Provider: Google Colab / Hugging Face
• Compute Region: Unknown
• Carbon Emitted: Not estimated

Technical Specifications [optional]

Model Architecture and Objective

Transformer-based causal language model

Objective: Next-token prediction optimized for numeric output consistency

Compute Infrastructure

[More Information Needed]

Hardware

NVIDIA GPU (T4, L4, A100-class)

Software

• Transformers

• PEFT

• PyTorch

Citation [optional]

BibTeX:

@misc{hong2025pricermerged, author = {MyungHwan Hong}, title = {Pricer Merged LLaMA 3.1 8B Model}, year = {2025}, url = {https://huggingface.co/MightyOctopus/pricer-merged-model-A-v1} }

APA:

MyungHwan Hong. (2025). Pricer Merged LLaMA 3.1 8B Model. Hugging Face.

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

MyungHwan Hong

Model Card Contact

Hugging Face: MightyOctopus