Delete host_weight/gen_csv.py

host_weight/gen_csv.py

@@ -1,30 +0,0 @@
-from transformers import AutoModelForCausalLM
-import argparse
-import numpy as np
-import os
-
-def write_csv_chunked(path, array, chunk_rows=1024):
-    with open(path, "w") as f:
-        n_rows = array.shape[0]
-        for start in range(0, n_rows, chunk_rows):
-            end = min(start + chunk_rows, n_rows)
-            np.savetxt(f, array[start:end], delimiter=",")
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Generate CSV file with model information.")
-    parser.add_argument("--model_dir", type=str, default="../../quant/hf_weight", help="Directory to save the model files.")
-    parser.add_argument("--emb", type=str, default="emb", help="Path to save the embedding CSV file.")
-    parser.add_argument("--whead", type=str, default="Whead_fp", help="Path to save the Whead CSV file.")
-    args = parser.parse_args()
-    model = AutoModelForCausalLM.from_pretrained(args.model_dir)
-
-    emb = model.model.embed_tokens.weight.detach().cpu().numpy()  # (V, H)
-    whead = model.lm_head.weight.detach().cpu().numpy()
-
-    emb_csv = args.emb + ".csv"
-    write_csv_chunked(emb_csv, emb)
-    print(f"Saved embeddings to {emb_csv}.")
-    whead_csv = args.whead + ".csv"
-    write_csv_chunked(whead_csv, whead)
-    print(f"Saved Whead to {whead_csv}.")
-    print("Conversion to binary completed.")