agoel7029/nano-weather-gpt-model

nanoWeatherGPT

A character-level GPT (~813K parameters) trained from scratch on synthetic weather forecast text.

Download & Use

from huggingface_hub import snapshot_download
import torch, pickle, sys

# Download all files
path = snapshot_download("agoel7029/nano-weather-gpt-model")
sys.path.insert(0, path)

from model import GPT

ckpt = torch.load(f"{path}/model.pt", map_location="cpu", weights_only=False)
meta = pickle.load(open(f"{path}/meta.pkl", "rb"))
stoi, itos = meta["stoi"], meta["itos"]

model = GPT(ckpt["config"])
model.load_state_dict(ckpt["model_state_dict"])
model.eval()

prompt = "Today the weather is"
ctx = torch.tensor([[stoi[c] for c in prompt]], dtype=torch.long)
with torch.no_grad():
    out = model.generate(ctx, max_new_tokens=200)
print("".join(itos[i] for i in out[0].tolist()))

Fine-tune on your own data

Edit train.py — replace generate_weather_corpus() with your own text, then run:

python train.py

Model details

Property	Value
Architecture	Character-level GPT (Transformer)
Parameters	~813K
Layers	4
Attention heads	4
Embedding dim	128
Context length	128 characters
Trained on	Synthetic Swiss city weather reports