README.md

---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.2-1B-Instruct
- lora
- transformers
---

# Model Card for Model ID

This is Llama 3.2 1B Instruct fine tuned for due process recognition performance. The tuned adapter was part of research evaluating the performance in recognizing legal due process issues by lower parameter models. 
With the adapter the model displays a score of 38% of responses being in the reasonable range (8+) in the Johnson and Lauf rubric as compared to only 4% of responses being reasonable with the reference model. 

Rubric scoring:

J. P. Johnson and A. P. Lauf, "Evaluating Large Language Model Understanding of Due Process," in 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI), 13-14 April 2024 2024, pp. 1-5, doi: 10.1109/ICMI60790.2024.10586176.

Brought to you by Josh at www.lexcygnus.com

## Model Details

### Model Description

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- **Developed by:** Joshua Johnson
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** J. P. Johnson and A. P. Lauf, "Improving Due Process Recognition in Low Parameter Models," Accepted for publication in the Proceedings of the 2025 IEEE SLAAI International Conference on Artificial Intelligence (ICAI 2025)
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

(https://huggingface.co/datasets/johnsonjp34/Due-Process-Training)


### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.16.0