pip install mlx-lm
mlx_lm.generate \
  --model Brandi-Kinard/plainspeak-smollm2-1.7b \
  --prompt "### Original:
YOUR TEXT HERE

### Plain English:" \
  --max-tokens 200

Examples

Shakespeare → Plain English

Original: "Wherefore art thou Romeo? Deny thy father and refuse thy name."

PlainSpeak: "Why are you Romeo? Don't deny your father and refuse your name."

Adam Smith → Plain English

Original: "The invisible hand of the market, whereby individuals pursuing their own self-interest are led, as if by an invisible hand, to promote ends which were no part of their original intention."

PlainSpeak: "When people try to make money for themselves, they often end up helping society without meaning to."

KJV Bible → Plain English

Original: "The LORD is my shepherd; I shall not want."

PlainSpeak: "The LORD leads me. I don't need anything else."

Model Details

Property	Value
Base model	SmolLM2-1.7B-Instruct
Fine-tuning method	LoRA (8 layers)
Training iterations	500
Training examples	1,200
Validation examples	150
Data source	Project Gutenberg + AI-generated synthetic pairs
Hardware	Apple M1, 16GB unified memory
Peak training memory	10.09 GB
Final val loss	1.771
Inference memory	~3.6 GB
Build time	1 evening

What it's good at

19th century prose (Dickens, James, Eliot, Hardy)
Shakespeare and Elizabethan English
King James Bible passages
Economic and political theory (Smith, Burke, Locke)
Academic abstracts
Legal boilerplate

Known limitations

Trained on 200-word chunks — short fragments may produce inconsistent results
Occasional errors on numerical content (dates, quantities)
Not optimized for highly technical scientific notation
May struggle with extremely abstract or experimental writing (e.g. stream-of-consciousness)

How it was built

1. Stream 1,500 prose passages from Project Gutenberg
2. Generate plain English versions using a frontier model as teacher
3. Format as (original → plain) training pairs
4. Fine-tune SmolLM2-1.7B with LoRA on Apple MLX
5. Fuse adapter into final weights

The key insight: a small model trained on 1,500 high-quality examples outperforms a large model trained on millions of noisy ones — at this specific task.

Use in Python

python

from mlx_lm import load, generate

model, tokenizer = load("Brandi-Kinard/plainspeak-smollm2-1.7b")

prompt = """### Original:
It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife.

### Plain English:"""

response = generate(model, tokenizer, prompt=prompt, max_tokens=200)
print(response)

Brandi-Kinard/plainspeak-smollm2-1.7b

PlainSpeak — Dense-to-Plain-English Translator

Try it