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File size: 2,498 Bytes
01938b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
import argparse
def fix_model(input_path, output_path):
print(f"Loading model from {input_path}...")
print("This may take a moment as we load it into RAM...")
# Load the model in bfloat16 to match the target format
model = AutoModelForCausalLM.from_pretrained(
input_path,
torch_dtype=torch.bfloat16,
device_map="cpu", # Load to CPU to avoid VRAM OOM during save
trust_remote_code=True
)
# Load tokenizer to ensure it carries over
try:
tokenizer = AutoTokenizer.from_pretrained(input_path)
except:
print("Warning: Could not load tokenizer. You may need to copy it manually.")
tokenizer = None
print("Forcing weight tying...")
# This is the magic command. It tells HF to treat embed_tokens and lm_head as the same object.
model.tie_weights()
# --- THE FIX FOR YOUR ERROR ---
# The error happens because the model config has conflicting settings.
# We force use_cache to True to satisfy the 'hybrid' cache implementation requirement.
print("Fixing Generation Config conflicts...")
model.config.use_cache = True
if model.generation_config is not None:
model.generation_config.use_cache = True
# ------------------------------
print(f"Saving fixed model to {output_path}...")
# max_shard_size="5GB" creates the standard 4-5 shard layout usually seen in 17.2GB models
model.save_pretrained(
output_path,
safe_serialization=True,
max_shard_size="5GB"
)
if tokenizer:
tokenizer.save_pretrained(output_path)
print("Done! The model should now be ~17.2GB and merge-compatible.")
if __name__ == "__main__":
# --- CONFIGURATION ---
# Use 'r' before the string to handle backslashes correctly on Windows
# 1. Where is the 18.9GB model? (Current folder)
input_model_path = r"A:\LLM\.cache\huggingface\hub\!models--sam-paech--Darkest-muse-v1"
# 2. Where do you want the fixed 17.2GB model?
output_model_path = r"A:\LLM\.cache\huggingface\hub\!models--sam-paech--Darkest-muse-v1\fixed"
# ---------------------
import os
if not os.path.exists(input_model_path):
print(f"Error: Input path '{input_model_path}' does not exist.")
else:
fix_model(input_model_path, output_model_path) |