MonumentalSystems/randygpt-ds-moe

randyGPT — model-ds-moe

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

Model Details

Architecture	MoE Transformer (causal LM)
Parameters	4.48M
Layers	12
Heads	4
Embedding dim	128
Experts	4 (top-2, dim 256)
Context window	256 tokens
Vocab size	2000 (BPE)
Training iters	12850
Best val loss	3.5012

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

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-ds-moe")
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 — a GPT implementation in Rust with Metal GPU acceleration.