This repository hosts a LoRA adapter that fine tunes meta-llama/Llama-3.2-1B-Instruct to rate charity purpose or mission statements on five scales: Specificity, Clarity, Impact, Inclusivity, and Attainable Goals.
The adapter is trained with MLX on Apple Silicon using the mlx-community/Llama-3.2-1B-Instruct-4bit base for low memory.

Note: This repo contains LoRA weights only. You will need the base model from Meta under the Llama 3.2 community license. See the license section below.

Intended use

Score short purpose or mission statements for charities and similar social purpose orgs.
Use in decision support or research workflows.
Not intended for real world compliance or automated decision making without human review.

How to use

MLX CLI

bash

# Inference with the adapter
mlx_lm generate   --model mlx-community/Llama-3.2-1B-Instruct-4bit   --adapter-path /path/to/adapter-folder   --temp 0.2   --max-tokens 120   --prompt 'You are a strict rater of charity purpose statements. Use only the 1 to 5 scale.

Rate the charity purpose statement on five 1 to 5 scales. Return only a compact JSON object with these integer keys: Specificity, Clarity, Impact, Inclusivity, "Attainable Goals". Do not include explanations.

Charity: Example Org
Statement: "We help young people into good jobs through mentoring and accredited training."'```

MLX Python

python

from mlx_lm import load, generate

MODEL = "mlx-community/Llama-3.2-1B-Instruct-4bit"
ADAPTER = "/path/to/adapter-folder"  # folder that contains adapters.safetensors

system = "You are a strict rater of charity purpose statements. Use only the 1 to 5 scale."
user = (
    'Rate the charity purpose statement on five 1 to 5 scales. '
    'Return only a compact JSON object with these integer keys: '
    'Specificity, Clarity, Impact, Inclusivity, "Attainable Goals". '
    'Do not include explanations.\n\n'
    'Charity: Example Org\n'
    'Statement: "We help young people into good jobs through mentoring and accredited training."'
)
prompt = f"{system}\n\n{user}"

model, tokenizer = load(MODEL, adapter_path=ADAPTER)
out = generate(model, tokenizer, prompt, max_tokens=120, temp=0.2)
print(out)

Prompt template used during training

System

You are a strict rater of charity purpose statements. Use only the 1 to 5 scale.

User

Rate the charity purpose statement on five 1 to 5 scales. Return only a compact JSON object with these integer keys: Specificity, Clarity, Impact, Inclusivity, "Attainable Goals". Do not include explanations.

Charity: {name}
Statement: "{text}"

Assistant target

{"Specificity": <int>, "Clarity": <int>, "Impact": <int>, "Inclusivity": <int>, "Attainable Goals": <int>}

Keep temperature low. 0.2 is a good default.

Training details

Base model: meta-llama/Llama-3.2-1B-Instruct
Format: Chat JSONL with messages entries
Optimizer: default MLX LoRA settings
Fine tune type: LoRA
Quantization: 4 bit base (mlx-community build)
Loss masking: prompts masked with --mask-prompt so loss applies to assistant tokens only

Hyperparameters

iters: 2200
batch size: 4
learning rate: 2e-5
max seq length: 512
grad checkpoint: on
steps per eval: 400
val batches: 5
save every: 200

Hardware

Apple Silicon arm64
Peak Metal memory reported near 2.6 GB with the settings above

Data

Source file: Charity_Purpose_Mission_with_Scores.csv
5 labels per example: Specificity, Clarity, Impact, Inclusivity, Attainable Goals
Train and validation split: 90 to 10
Approx counts: 8,826 train and 981 validation
Data is not redistributed in this repo

Evaluation

A simple validation check reports mean absolute error per label. You can reproduce quickly by sampling valid.jsonl and generating with temperature 0.2, then parsing the JSON.

Suggested metrics

MAE per label
Exact integer match rate per label
JSON validity rate

Limitations

The model learns from short statements and may not generalize to long narratives.
Scores are subjective and depend on the rubric implied by the data.
The adapter is not calibrated for downstream decisions. Human review is required.
English only.

Safety and bias

Statements about social impact and inclusion can reflect biases present in the data.
Use with caution in any sensitive or high stakes decision process.
Always keep a human in the loop.

License

Base model: Meta Llama 3.2 community license terms apply. You must accept the license on the Meta model page to download the base.
This repo contains LoRA weights derived from that base. Redistribution of the base weights is not included here.
Dataset is proprietary to the dataset owner. Not included in this repo.

Acknowledgements

Meta for Llama 3.2
Apple MLX team and mlx-community for the MLX model builds

Contact

Maintainer: www.trsutimpact.com
Questions or issues: open a Discussion on the Hugging Face repo

Miemczyk/CharityPurposeAnalyser

Charity Purpose Rater - Llama 3.2 1B Instruct - MLX LoRA