Instructions to use adyoi/indigo.tf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use adyoi/indigo.tf with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://adyoi/indigo.tf") - Notebooks
- Google Colab
- Kaggle
| import json | |
| class CharTokenizer: | |
| def __init__(self, vocab=None): | |
| if vocab is None: | |
| self.itos = [] | |
| else: | |
| self.itos = list(vocab) | |
| self.stoi = {ch: i for i, ch in enumerate(self.itos)} | |
| def from_text(cls, text): | |
| return cls(sorted(set(text))) | |
| def vocab_size(self): | |
| return len(self.itos) | |
| def encode(self, text): | |
| return [self.stoi[ch] for ch in text if ch in self.stoi] | |
| def decode(self, ids): | |
| return "".join(self.itos[i] for i in ids) | |
| def save(self, path): | |
| with open(path, "w", encoding="utf-8") as f: | |
| json.dump(self.itos, f, ensure_ascii=False) | |
| def load(cls, path): | |
| with open(path, encoding="utf-8") as f: | |
| return cls(json.load(f)) | |