---
language:
- en
license: mit
tags:
- text-generation
- fine-tuning
- lora
- mlx
- plain-english
- literary
- translation
- smollm2
base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
---

# 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](https://huggingface.co/spaces/Brandi-Kinard/plainspeak)**

```bash
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)
```

## Links

- [GitHub repo](https://github.com/Brandi-Kinard/plainspeak)
- [Live demo](https://huggingface.co/spaces/Brandi-Kinard/plainspeak)
- [Built by Brandi Kinard](https://www.linkedin.com/in/brandi-kinard/)