---
language: en
license: mit
tags:
  - text-generation
  - causal-lm
  - randygpt
  - rust
---

# randyGPT — model-ds2

A GPT-style language model trained from scratch in Rust on Project Gutenberg.

## Model Details

| | |
|---|---|
| Architecture | Transformer (causal LM) |
| Parameters | 2.90M |
| Layers | 12 |
| Heads | 4 |
| Embedding dim | 128 |
| Context window | 256 tokens |
| Vocab size | 2000 (BPE) |
| Training iters | 14375 |
| Best val loss | 3.8242 |

## Training

Trained on ~98MB of cleaned Project Gutenberg text (112 public domain books,
v3 cleaning with Unicode normalization) with BPE-2000 tokenization,
AdamW optimizer, cosine LR decay, ReduceLROnPlateau, dropout=0.1, and
Metal GPU via Candle on Apple Silicon.

## Usage

```python
from modeling_randygpt import RandyGPTConfig, RandyGPTForCausalLM
from tokenizer_randygpt import RandyGPTTokenizer
from safetensors.torch import load_file
import torch

# Load
cfg   = RandyGPTConfig.from_pretrained("MonumentalSystems/randygpt-ds2")
model = RandyGPTForCausalLM(cfg)
state = load_file("model.safetensors")
model.load_state_dict(state, strict=True)
model.eval()

tok = RandyGPTTokenizer.from_file("tokenizer.json")

# Generate
prompt  = "Once upon a time"
ids     = torch.tensor([tok.encode(prompt)], dtype=torch.long)
out_ids = model.generate_text(ids, max_new_tokens=200, temperature=0.8)
print(tok.decode(out_ids[0].tolist()))
```

## Source

Trained with [randyGPT](https://github.com/MonumentalSystems/RandyGPT) —
a GPT implementation in Rust with Metal GPU acceleration.