Delete generate_tf.py with huggingface_hub

generate_tf.py

@@ -1,68 +0,0 @@
-import argparse
-import os
-import random
-
-import numpy as np
-import tensorflow as tf
-
-from indigo.common import load_meta
-from indigo.model_keras import build_gpt, generate
-from indigo.tokenizer import CharTokenizer
-
-CONFIG_KEYS = ("vocab_size", "block_size", "n_layer", "n_head", "n_embd", "dropout")
-
-
-def load_model(path):
-    from safetensors.numpy import load_file
-
-    meta = load_meta(path)
-    if meta.get("backend") != "tensorflow":
-        raise SystemExit(
-            f"{path} berasal dari backend {meta.get('backend')}, gunakan generate.py (PyTorch)"
-        )
-    state = load_file(path)
-    by_path = {k.replace("/", "_"): v for k, v in state.items()}
-    model = build_gpt(**{k: meta["config"][k] for k in CONFIG_KEYS})
-    missing = [v.path for v in model.weights if v.path.replace("/", "_") not in by_path]
-    if missing:
-        raise SystemExit(f"bobot tidak cocok dengan checkpoint: {missing[:5]}")
-    model.set_weights([by_path[v.path.replace("/", "_")] for v in model.weights])
-    return model, meta
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Generate teks dari checkpoint Indigo (TensorFlow)")
-    parser.add_argument("--ckpt", default="out_tf/indigo_best.safetensors")
-    parser.add_argument("--prompt", default="")
-    parser.add_argument("--max-new", type=int, default=300)
-    parser.add_argument("--temperature", type=float, default=0.8)
-    parser.add_argument("--top-k", type=int, default=40)
-    parser.add_argument("--seed", type=int, default=None)
-    parser.add_argument("--device", default="auto", choices=["auto", "cpu", "gpu"])
-    args = parser.parse_args()
-
-    if args.device == "cpu":
-        tf.config.set_visible_devices([], "GPU")
-    if args.seed is not None:
-        random.seed(args.seed)
-        np.random.seed(args.seed)
-        tf.random.set_seed(args.seed)
-
-    model, meta = load_model(args.ckpt)
-    tokenizer = CharTokenizer(meta["vocab"])
-
-    ids = tokenizer.encode(args.prompt) or [0]
-    idx = tf.constant([ids], dtype=tf.int64)
-    out = generate(
-        model,
-        idx,
-        args.max_new,
-        block_size=meta["config"]["block_size"],
-        temperature=args.temperature,
-        top_k=args.top_k,
-    )
-    print(tokenizer.decode(out.numpy()[0].tolist()))
-
-
-if __name__ == "__main__":
-    main()