Fix saving 4bit for VLM

tests/saving/test_unsloth_save.py

@@ -0,0 +1,169 @@
+import json
+import os
+import shutil
+import tempfile
+import pytest
+
+from unsloth import FastLanguageModel, FastModel
+
+model_to_test = [
+    # Text Models
+    "unsloth/tinyllama",
+    "unsloth/tinyllama-bnb-4bit",
+    "unsloth/Qwen2.5-0.5B-Instruct",
+    "unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit",
+    "unsloth/Phi-4-mini-instruct",
+    "unsloth/Phi-4-mini-instruct-bnb-4bit",
+    "unsloth/Qwen2.5-0.5B",
+    # Vision Models
+    "unsloth/gemma-3-1b-it",
+    "unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit",
+    "unsloth/Qwen2.5-VL-3B-Instruct-bnb-4bit"
+]
+
+# Variables
+save_file_sizes = {}
+save_file_sizes["merged_16bit"] = {}
+save_file_sizes["merged_4bit"] = {}
+
+tokenizer_files = [
+    "tokenizer_config.json",
+    "special_tokens_map.json",
+]
+
+@pytest.fixture(scope="session", params=model_to_test)
+def loaded_model_tokenizer(request):
+    model_name = request.param
+    print("Loading model and tokenizer...")
+
+    model, tokenizer = FastModel.from_pretrained(
+        model_name, # use small model
+        max_seq_length=128,
+        dtype=None,
+        load_in_4bit=True,
+    )
+
+    # Apply LoRA
+    model = FastModel.get_peft_model(
+        model,
+        r=16,
+        target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+        lora_alpha=16,
+        use_gradient_checkpointing="unsloth",
+    )
+
+    return model, tokenizer
+
+@pytest.fixture(scope="session")
+def model(loaded_model_tokenizer):
+    return loaded_model_tokenizer[0]
+
+@pytest.fixture(scope="session")
+def tokenizer(loaded_model_tokenizer):
+    return loaded_model_tokenizer[1]
+
+@pytest.fixture
+def temp_save_dir():
+    dir = tempfile.mkdtemp()
+    print(f"Temporary directory created at: {dir}")
+    yield dir
+    print(f"Temporary directory deleted: {dir}")
+    shutil.rmtree(dir)
+
+
+def delete_quantization_config(model):
+    # Since merged, edit quantization_config
+    old_config = model.config
+    new_config = model.config.to_dict()
+    if "quantization_config" in new_config:
+        del new_config["quantization_config"]
+    original_model = model
+    new_config = type(model.config).from_dict(new_config)
+    while hasattr(original_model, "model"):
+        original_model = original_model.model
+        original_model.config = new_config
+    model.config = new_config
+
+def test_save_merged_16bit(model, tokenizer, temp_save_dir: str):
+    save_path = os.path.join(temp_save_dir, "unsloth_merged_16bit", model.config._name_or_path.replace("/", "_"))
+
+    model.save_pretrained_merged(
+        save_path,
+        tokenizer=tokenizer,
+        save_method="merged_16bit"
+    )
+
+    # Check model files
+    assert os.path.isdir(save_path), f"Directory {save_path} does not exist."
+    assert os.path.isfile(os.path.join(save_path, "config.json")), "config.json not found."
+
+    weight_files = [f for f in os.listdir(save_path) if f.endswith(".bin") or f.endswith(".safetensors")]
+    assert len(weight_files) > 0, "No weight files found in the save directory."
+
+    # Check tokenizer files
+    for file in tokenizer_files:
+        assert os.path.isfile(os.path.join(save_path, file)), f"{file} not found in the save directory."
+
+    # Check config to see if it is 16bit by checking for quantization config
+    config_path = os.path.join(save_path, "config.json")
+    with open(config_path, "r") as f:
+        config = json.load(f)
+
+    assert "quantization_config" not in config, "Quantization config not found in the model config."
+
+    # Store the size of the model files
+    total_size = sum(os.path.getsize(os.path.join(save_path, f)) for f in weight_files)
+    save_file_sizes["merged_16bit"][model.config._name_or_path] = total_size
+    print(f"Total size of merged_16bit files: {total_size} bytes")
+
+    # Test loading the model from the saved path
+    loaded_model, loaded_tokenizer = FastLanguageModel.from_pretrained(
+        save_path,
+        max_seq_length=128,
+        dtype=None,
+        load_in_4bit=True,
+    )
+
+def test_save_merged_4bit(model, tokenizer, temp_save_dir: str):
+    save_path = os.path.join(temp_save_dir, "unsloth_merged_4bit", model.config._name_or_path.replace("/", "_"))
+
+    model.save_pretrained_merged(
+        save_path,
+        tokenizer=tokenizer,
+        save_method="merged_4bit_forced"
+    )
+
+    # Check model files
+    assert os.path.isdir(save_path), f"Directory {save_path} does not exist."
+    assert os.path.isfile(os.path.join(save_path, "config.json")), "config.json not found."
+
+    weight_files = [f for f in os.listdir(save_path) if f.endswith(".bin") or f.endswith(".safetensors")]
+    assert len(weight_files) > 0, "No weight files found in the save directory."
+
+    # Check tokenizer files
+    for file in tokenizer_files:
+        assert os.path.isfile(os.path.join(save_path, file)), f"{file} not found in the save directory."
+
+    # Store the size of the model files
+    total_size = sum(os.path.getsize(os.path.join(save_path, f)) for f in weight_files)
+    save_file_sizes["merged_4bit"][model.config._name_or_path] = total_size
+
+    print(f"Total size of merged_4bit files: {total_size} bytes")
+
+    assert total_size < save_file_sizes["merged_16bit"][model.config._name_or_path], "Merged 4bit files are larger than merged 16bit files."
+
+    # Check config to see if it is 4bit
+    config_path = os.path.join(save_path, "config.json")
+    with open(config_path, "r") as f:
+        config = json.load(f)
+
+    assert "quantization_config" in config, "Quantization config not found in the model config."
+
+    # Test loading the model from the saved path
+    loaded_model, loaded_tokenizer = FastModel.from_pretrained(
+        save_path,
+        max_seq_length=128,
+        dtype=None,
+        load_in_4bit=True,
+    )
+

unsloth/save.py

@@ -2301,6 +2301,17 @@ def unsloth_generic_save(
     maximum_memory_usage : float = 0.9,
 ):
     if token is None and push_to_hub: token = get_token()
+
+    if save_method == "merged_4bit":
+        raise RuntimeError(
+            "Unsloth: Merging into 4bit will cause your model to lose accuracy if you plan\n"\
+            "to merge to GGUF or others later on. I suggest you to do this as a final step\n"\
+            "if you're planning to do multiple saves.\n"\
+            "If you are certain, change `save_method` to `merged_4bit_forced`."
+        )
+    elif save_method == "merged_4bit_forced":
+        save_method = "merged_4bit"
+
     merge_and_overwrite_lora(
         get_model_name,
         model                = model,
@@ -2309,6 +2320,7 @@ def unsloth_generic_save(
         push_to_hub          = push_to_hub,
         private              = private,
         token                = token,
+        save_method          = save_method,
         output_dtype         = None,
         low_disk_space_usage = True,
         use_temp_file        = False,