Upload fp32_to_fp16.py

fp32_to_fp16.py

@@ -0,0 +1,40 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+
+# --- YOU MUST UPDATE THESE TWO PATHS ---
+# Path to the directory where your FP32 model is stored locally
+input_dir = "A:\LLM\.cache\huggingface\hub\models--wzhouad--gemma-2-9b-it-WPO-HB"
+
+# Path to the directory where the converted FP16 model will be saved
+output_dir = "A:\LLM\.cache\huggingface\hub\models--wzhouad--gemma-2-9b-it-WPO-HB_FP16"
+# -------------------------------------
+
+# Make sure the output directory exists
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+# Load the tokenizer from the local path
+print(f"Loading tokenizer from {input_dir}...")
+tokenizer = AutoTokenizer.from_pretrained(input_dir)
+
+# Load the model in FP32 from the local path
+print(f"Loading FP32 model from {input_dir}...")
+model = AutoModelForCausalLM.from_pretrained(
+    input_dir,
+    torch_dtype=torch.float32,
+    device_map="cpu"
+    # device_map="auto" # use this if you have enough GPU VRAM
+)
+
+# Convert the model to FP16 and save it to the new local directory
+print("Converting model to FP16 and saving to disk...")
+model.half().save_pretrained(
+    output_dir,
+    safe_serialization=True,
+    max_shard_size="5GB"
+)
+tokenizer.save_pretrained(output_dir)
+
+print(f"Model successfully converted and saved to {output_dir}")
+print("You can now use this new FP16 model in your mergekit config.yaml.")