This is a 12-layer decoder-only Transformer trained on the openvision dataset for 1.0 hours of wall-clock training time. The model achieves a validation bits-per-byte (val_bpb) of 4.302087 (perplexity: 19.7268) on the held-out validation set.
Data is packed into fixed-length sequences of 2048 tokens using the nanochat-compatible BPE tokenizer (16,384 vocabulary, 9 special tokens). No additional filtering or deduplication is applied beyond what is in the source dataset.
1import torch
2import pickle
3import json
4from train import GPT, GPTConfig, Tokenizer
5
6# Load config
7with open('config.json', 'r') as f:
8 config_dict = json.load(f)
9config = GPTConfig(**{k: v for k, v in config_dict.items() if k in GPTConfig.__dataclass_fields__})
10
11# Load model
12model = GPT(config)
13state_dict = torch.load('model.pt', map_location='cpu')['state_dict']
14model.load_state_dict(state_dict)
15model.eval()
16
17# Load tokenizer
18with open('tokenizer.pkl', 'rb') as f:
19 tokenizer = pickle.load(f)
20
21# Generate
22prompt = 'Once upon a time, '
23input_ids = tokenizer.encode(prompt)
24x = torch.tensor([input_ids], dtype=torch.long)
25with torch.no_grad():
26 for _ in range(50):
27 logits = model(x)
28 probs = torch.softmax(logits[:, -1, :] / 0.8, dim=-1)
29 next_token = torch.multinomial(probs, num_samples=1)
30 input_ids.append(next_token.item())
31 x = torch.tensor([input_ids], dtype=torch.long)
32print(tokenizer.decode(input_ids))
1model.pt # Model weights
2config.json # Model architecture config
3dataset.txt # Dataset name used for training
4token_bytes.pt # Token byte mappings
5tokenizer.pkl # Trained BPE tokenizer
6tokenizer_config.json # Tokenizer configuration
7training_metrics.json # Training metrics
8README.md # This file
1@misc{autoresearch_openvision_depth12,
2 title={AutoResearch-openvision-depth12},
3 author={Dustin Loring},
4 year={2026},
5 howpublished={\url{https://huggingface.co/quik-models/sage-wind-48}}
6}}
