Save processor in quantizer CLI (#3290)

src/axolotl/cli/quantize.py

@@ -8,7 +8,7 @@ from typing import Union
 from transformers import AutoConfig, AutoModelForCausalLM, TorchAoConfig
 
 from axolotl.cli.config import load_cfg
-from axolotl.loaders import load_tokenizer
+from axolotl.loaders import load_processor, load_tokenizer
 from axolotl.utils.logging import get_logger
 from axolotl.utils.quantization import (
     TorchAOQuantDType,
@@ -66,6 +66,11 @@ def do_quantize(
 
     LOG.info(f"Loading model from {model_path}.")
     tokenizer = load_tokenizer(cfg)
+
+    processor = None
+    if cfg.is_multimodal:
+        processor = load_processor(cfg, tokenizer)
+
     config = AutoConfig.from_pretrained(model_path)
     torch_dtype = config.torch_dtype if hasattr(config, "torch_dtype") else None
     model = AutoModelForCausalLM.from_pretrained(
@@ -107,6 +112,10 @@ def do_quantize(
         save_jinja_files=cfg.tokenizer_save_jinja_files,
     )
 
+    if processor:
+        LOG.info(f"Saving processor to: {str(Path(output_dir) / 'quantized')}.")
+        processor.save_pretrained(str(Path(output_dir) / "quantized"))
+
     if hub_model_id:
         hub_model_id = (
             hub_model_id.rstrip("-")
@@ -114,6 +123,8 @@ def do_quantize(
         )
         model.push_to_hub(hub_model_id, safe_serialization=False)
         tokenizer.push_to_hub(hub_model_id)
+        if processor:
+            processor.push_to_hub(hub_model_id)
         LOG.info(f"Quantized model pushed to: {hub_model_id}.")
 
     LOG.info(f"Quantized model saved to: {str(Path(output_dir) / 'quantized')}.")