---
language:
- en
license: mit
library_name: mlx-lm
tags:
- disinformation
- election-security
- content-moderation
- nlp
- llm
- fine-tuned
- lora
- disarm-framework
- election-watch
- misinformation-detection
- social-media-analysis
- nigerian-elections
- meta-narratives
- conspiracy-theories
- electoral-integrity
datasets:
- ArapCheruiyot/disarm-election-watch-dataset
metrics:
- training_loss: 1.064
- validation_loss: 1.354
- training_iterations: 600
- training_examples: 6019
- validation_examples: 601
- test_examples: 603
pipeline_tag: text-generation
---

# DISARM Election Watch - Fine-tuned Llama-3.1 Model

## Model Description

This is a fine-tuned version of the Llama-3.1 model specifically optimized for DISARM Framework analysis of election-related content. The model has been trained on a comprehensive dataset of Nigerian election content from multiple platforms to identify and classify disinformation, misinformation, and coordinated influence operations.

### Model Details

- **Base Model**: [ArapCheruiyot/disarm_ew-llama3](https://huggingface.co/ArapCheruiyot/disarm_ew-llama3)
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Optimization**: Apple Silicon (M1 Max) optimized
- **Training Data**: 6,019 examples from multiple sources
- **Task**: DISARM Framework classification and narrative analysis
- **Language**: English
- **License**: MIT

### Training Configuration

- **LoRA Rank**: 16
- **Batch Size**: 1
- **Learning Rate**: 3e-4
- **Sequence Length**: 2048
- **Training Iterations**: 600
- **Final Training Loss**: 1.064
- **Final Validation Loss**: 1.354
- **Framework**: MLX-LM
- **Hardware**: Apple M1 Max (64GB RAM)

## Quick Start

### Using with MLX-LM

```python
from mlx_lm import load, generate

# Load the complete fine-tuned model
model, tokenizer = load("models/disarm_ew_llama3_finetuned")

# Example prompt
prompt = """### Instruction:
Classify the following content according to DISARM Framework techniques and meta-narratives:

### Input:
A viral WhatsApp broadcast claims that the BVAS machines have been pre-loaded with votes by INEC in favour of the incumbent party.

### Response:"""

# Generate response
response = generate(model, tokenizer, prompt, max_tokens=256, temp=0.1)
print(response)
```

### Using with Ollama

```bash
# Create Ollama model
ollama create disarm-ew-llama3-finetuned -f Modelfile

# Run the model
ollama run disarm-ew-llama3-finetuned "Your prompt here"
```

### Example Usage

```bash
ollama run disarm-ew-llama3-finetuned "### Instruction:
Classify the following content according to DISARM Framework techniques and meta-narratives:

### Input:
A viral WhatsApp broadcast claims that the BVAS machines have been pre-loaded with votes by INEC in favour of the incumbent party.

### Response:"
```

### Expected Output

```json
{
  "meta_narrative": "Compromised Election Technology",
  "primary_disarm_technique": "T0022.001: Develop False Conspiracy Theory Narratives about Electoral Manipulation and Compromise",
  "confidence_score": 0.98,
  "key_indicators": ["BVAS", "pre-loaded", "INEC"],
  "platform": "WhatsApp",
  "language": "en",
  "category": "Undermining Electoral Institutions"
}
```

## Performance

### Training Performance
- **Training Loss**: 1.064
- **Validation Loss**: 1.354
- **Training Speed**: ~1.16 iterations/second
- **Memory Usage**: 19.161 GB peak during training

### Inference Performance
- **Inference Speed**: ~20 tokens/second
- **Memory Usage**: 16.149 GB during inference
- **Model Size**: 16GB (fused), 1.7MB (LoRA adapters)

### Hardware Optimization
- **Apple Silicon**: Optimized for M1 Max
- **Metal GPU**: Accelerated inference
- **Memory Management**: 16GB wired memory optimization

## Model Files

### Fused Model (Complete)
- **Size**: 16GB
- **Format**: MLX-LM safetensors
- **Files**: 4 model weight files + configuration

### LoRA Adapters (Lightweight)
- **Size**: 1.7MB
- **Format**: safetensors
- **Files**: Final adapters + training checkpoints

## Local Deployment Benefits

- **Privacy**: Run locally without sending data to external servers
- **Speed**: Fast inference on local hardware
- **Customization**: Modify prompts and parameters as needed
- **Offline**: Works without internet connection

## Contact

For questions, issues, or collaboration opportunities:
- **Model Repository**: [ArapCheruiyot/disarm-ew-llama3-finetuned](https://huggingface.co/ArapCheruiyot/disarm-ew-llama3-finetuned)
- **Dataset Repository**: [ArapCheruiyot/disarm-election-watch-dataset](https://huggingface.co/datasets/ArapCheruiyot/disarm-election-watch-dataset)