---
model-index:
- name: augustulus-latin-sentiment-lora
  results:
  - task:
      type: text-classification
      name: Sentiment Analysis
    dataset:
      name: Ancient Latin Sentiment (Custom)
      type: custom
    metrics:
    - type: accuracy
      value: 75
      name: Accuracy (with linguistic post-processing)
    - type: accuracy
      value: 37.5
      name: Raw Model Accuracy
license: llama3.1
language:
- la
base_model:
- meta-llama/Llama-3.1-8B-Instruct
tags:
- gguf
- quantized
- llama-cpp
---

# Augustulus Latin Sentiment Analysis LoRA

**Developed by Team Trojan Parse** *University of Florida Senior Design Project*

A LoRA (Low-Rank Adaptation) adapter fine-tuned on **Llama-3.1-8B-Instruct** for fine-grained sentiment classification of Ancient Latin texts across seven emotional intensity levels.

## Project Information

- **Team Name:** Trojan Parse
- **Team Members:**
  - Alex John
  - Ryan Willson
  - Byron Boatright
  - Jake Marotta
  - Duncan Fuller
- **Project Repository:** [GitHub: Trojan-Parse-Project](https://github.com/alxxjohn/Trojan-Parse-Project)
- **Advisor:** Eleni Bozia, Ph.D., Dr. phil. (Associate Professor of Classics and Digital Humanities)
- **Advisor Department:** Department of Classics, University of Florida

## Model Description

- **Model Name:** `augustulus-latin-sentiment-lora`
- **Model type:** LoRA Adapter for Ancient Language Sentiment Classification
- **Language:** Classical/Ancient Latin
- **Base model:** [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
- **License:** Llama 3.1 Community License
- **Purpose:** Academic research and historical text analysis

## Sentiment Categories

Our model classifies Ancient Latin texts into six emotional intensity levels:

### Positive Sentiments
- **EXTREMELY POSITIVE (+3)**: *exsultatio, jubilum, beatitudo, summa felicitas*
  - Examples: Triumphal declarations, ultimate joy, divine blessing
  
- **VERY POSITIVE (+2)**: *gaudium, laetitia, amor, gloria, victoria, laudare*
  - Examples: Military victories, celebrations, expressions of love/honor
  
- **MODERATELY POSITIVE (+1)**: *felix, laetus, bonus, pulcher, spes*
  - Examples: General contentment, hope, pleasant situations

### Neutral (0)
- Factual statements, descriptions without emotional valence

### Negative Sentiments
- **MODERATELY NEGATIVE (-1)**: *malus, tristis, anxius, timor*
  - Examples: Minor concerns, sadness, mild fear
  
- **VERY NEGATIVE (-2)**: *dolor magnus, timor vehemens, ira, furor*
  - Examples: Great pain, intense anger, serious threats
  
- **EXTREMELY NEGATIVE (-3)**: *desperatio, exitium, cruciatus, malum*
  - Examples: Utter despair, destruction, torture, ultimate evil

## Performance

| Configuration | Accuracy | Notes |
|:---|:---:|:---|
| Base Llama 3.1 (zero-shot) | 43.8% | Unreliable, biased toward extremes |
| LoRA Adapter (raw predictions) | 37.5% | Systematic but conservative |
| **LoRA + Linguistic Rules** | **75.0%** | Production-ready |

### Category-Level Performance
- **Neutral Detection:** 100% accuracy (3/3 test cases)
- **Moderate Categories:** 100% accuracy (learned systematic patterns)
- **Extreme Categories:** 83.3% accuracy (with intensity calibration)

## Training Approach

Our training methodology combined multiple data sources and validation strategies:

### Data Pipeline (5-day development cycle)

**Phase 1: Initial Generation**
- Few-shot generation using base Llama 3.1
- Context-aware synthetic examples
- Balanced across all six sentiment categories

**Phase 2: Consensus Filtering**
- Trained multiple LoRA variants on hand-annotated data
- Consensus filtering: kept examples where ≥2 models agreed
- Reduced noise and improved training data quality

**Phase 3: Corpus Mining**
- Mined authentic Ancient Latin texts from Perseus Digital Library
- Extracted high-confidence positive examples (previously underrepresented)
- Combined ~40,000 corpus examples with synthetic data

**Phase 4: Final Training & Iteration**
- Balanced dataset: 9,000 examples (1,500 per category)
- Distributed training with data-parallel strategy
- Multiple training runs to optimize hyperparameters

### Final Training Configuration
- **Training Examples:** 9,000 (balanced across 7 categories)
- **Training Epochs:** 15
- **Architecture:** LoRA adapter (rank: 128, alpha: 256)
- **Optimization:** 8-bit quantization for efficiency
- **Hardware:** High-performance GPU cluster
- **Framework:** PyTorch, HuggingFace Transformers, PEFT

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model and adapter
base_model = "meta-llama/Llama-3.1-8B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True
)

# Load Team Trojan Parse's adapter
# Replace YOUR_USERNAME with your Hugging Face username
model = PeftModel.from_pretrained(model, "YOUR_USERNAME/augustulus-latin-sentiment-lora")
tokenizer = AutoTokenizer.from_pretrained(base_model)

# Classify sentiment
def classify_latin_sentiment(text):
    prompt = f'''Classify the sentiment of this Latin text as: VERY NEGATIVE, MODERATELY NEGATIVE, NEUTRAL, MODERATELY POSITIVE, VERY POSITIVE, or EXTREMELY POSITIVE.

Latin text: {text}

Sentiment:'''
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=20,
        temperature=0.1,
        do_sample=False
    )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("Sentiment:")[-1].strip()

# Example: Extreme positive (triumph)
text = "Victoria splendidissima! Dux gloriam aeternam meruit!"
print(classify_latin_sentiment(text))
# Output: EXTREMELY POSITIVE

# Example: Extreme negative (despair)
text = "Bellum crudele et longum populum afflixerat."
print(classify_latin_sentiment(text))
# Output: VERY NEGATIVE
```

---

## GGUF Model Download and Local Usage (Merged Fine-Tune)

The LoRA adapter has been merged with the base model and quantized to **Q8_0** (8-bit) precision for efficient deployment on CPU/GPU via tools like `llama.cpp` and `Ollama`.

### 💾 File Details
- **File Name:** `augustulus-latin-sentiment-8b-q8_0.gguf`
- **Size:** 8.0 GB
- **Quantization:** Q8_0 (Recommended for best balance of speed and accuracy)

---

### Llama 3.1 License Notice
**IMPORTANT**: This model (including the GGUF file) is a derivative of Meta’s **Llama 3.1** model and is governed by the [**Meta Llama 3.1 Community License**](https://llama.meta.com/llama3_1/license).

- **Attribution**: If you redistribute or build products with this model, you must include the statement **“Built with Meta Llama 3”** in a prominent location (e.g., README, UI footer, about page).
- **Commercial Use**: Allowed without additional permission as long as your product or service has **fewer than 700 million monthly active users**. Above that threshold, you need a separate commercial license from Meta.

See the full license text here: https://llama.meta.com/llama3_1/license
---

### Usage Example (with Ollama)
This workflow uses a custom **Modelfile** to set the strict sentiment task and gives the model a simple local name.

#### Create Modelfile
Save the following content as a file named `Modelfile`:

```text
# Modelfile for the Augustulus Latin Sentiment Model

FROM hf.co/TronCodes/augustulus-latin-sentiment-lora/augustulus-latin-sentiment-8b-q8_0.gguf

SYSTEM """
You are Augustulus, an expert in Classical Latin sentiment analysis. Your task is to respond ONLY with one of the following exact labels: EXTREMELY POSITIVE, VERY POSITIVE, MODERATELY POSITIVE, NEUTRAL, MODERATELY NEGATIVE, VERY NEGATIVE, or EXTREMELY NEGATIVE. Do not provide any conversational text or explanation.
"""

TEMPLATE """
{{ if .System }}<|start_header_id|>system<|end_header_id|>{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
"""

PARAMETER temperature 0.1
PARAMETER num_predict 20
PARAMETER stop "<|eot_id|>"
```

---

#### Create and Run
```bash
ollama create augustulus-latin -f Modelfile
ollama run augustulus-latin
```


## Acknowledgments

We gratefully acknowledge:
* Dr. Eleni Bozia (Ph.D., Dr. phil.) - Senior Project Advisor
* University of Florida Department of Humanities - Computing resources and support
* Perseus Digital Library - Access to Classical Latin corpus
* Meta AI - Llama 3.1 base model
* HuggingFace - PEFT library and model hosting infrastructure

## Citation

```bibtex
@misc{trojan_parse_latin_sentiment_2025,
  author = {{Team Trojan Parse}},
  title = {Augustulus Latin Sentiment Analysis LoRA},
  year = {2025},
  publisher = {University of Florida},
  journal = {HuggingFace Model Hub},
  howpublished = {\\url{[https://huggingface.co/TronCodes/augustulus-latin-sentiment-lora](https://huggingface.co/TronCodes/augustulus-latin-sentiment-lora)}}
}