PlainSpeak — Dense-to-Plain-English Translator
I taught a tiny AI to speak human.
Give it Shakespeare. Give it a legal contract. Give it anything written to impress instead of communicate. It gives you back what it actually means.
Try it
→ Live demo on Hugging Face Spaces
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
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)
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HuggingFaceTB/SmolLM2-1.7B