From efe2cc43a75d1de599fbee623581115bbacffbb2 Mon Sep 17 00:00:00 2001
From: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Date: Thu, 12 Jun 2025 00:08:41 +0300
Subject: [PATCH] tests for additional merge fix unsloth zoo pr 163 (#2719)

* tests for additional merge fix unsloth zoo pr 163

* fixed load_dataset indent in mistral perplexity test file
---
 .../test_merge_model_perplexity_llama-3.2.py  |   4 +-
 .../test_merge_model_perplexity_mistral.py    |   3 +-
 .../test_merge_model_perplexity_phi_4.py      |   4 +-
 ...st_merged_model_perplexity_llama-3.1-8b.py |   3 +-
 .../test_merged_model_perplexity_qwen_2.5.py  |   3 +-
 .../test_push_to_hub_merged.py                |   3 +-
 ...t_push_to_hub_merged_sharded_index_file.py |   3 +-
 .../test_save_merged_grpo_model.py            |   3 +-
 .../saving/non_peft/test_mistral_non_peft.py  |  67 +++++
 .../saving/non_peft/test_whisper_non_peft.py  |  67 +++++
 .../saving/text_to_speech_models/test_csm.py  | 156 +++++++++++
 .../saving/text_to_speech_models/test_lasa.py | 217 +++++++++++++++
 .../text_to_speech_models/test_orpheus.py     | 254 ++++++++++++++++++
 .../text_to_speech_models/test_whisper.py     | 189 +++++++++++++
 .../test_index_file_sharded_model.py          |   3 +-
 .../vision_models/test_push_to_hub_merged.py  |   4 +-
 ...t_save_merge_vision_model_ocr_benchmark.py |   3 +-
 tests/utils/os_utils.py                       | 119 ++++++++
 18 files changed, 1094 insertions(+), 11 deletions(-)
 create mode 100644 tests/saving/non_peft/test_mistral_non_peft.py
 create mode 100644 tests/saving/non_peft/test_whisper_non_peft.py
 create mode 100644 tests/saving/text_to_speech_models/test_csm.py
 create mode 100644 tests/saving/text_to_speech_models/test_lasa.py
 create mode 100644 tests/saving/text_to_speech_models/test_orpheus.py
 create mode 100644 tests/saving/text_to_speech_models/test_whisper.py
 create mode 100644 tests/utils/os_utils.py

diff --git a/tests/saving/language_models/test_merge_model_perplexity_llama-3.2.py b/tests/saving/language_models/test_merge_model_perplexity_llama-3.2.py
index 384da30e85..2d4ec83566 100644
--- a/tests/saving/language_models/test_merge_model_perplexity_llama-3.2.py
+++ b/tests/saving/language_models/test_merge_model_perplexity_llama-3.2.py
@@ -14,8 +14,10 @@ import gc
 import sys
 from pathlib import Path
 
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
+
 
 from tests.utils.cleanup_utils import safe_remove_directory
 from tests.utils.perplexity_eval import ppl_model, add_to_comparison, print_model_comparison
diff --git a/tests/saving/language_models/test_merge_model_perplexity_mistral.py b/tests/saving/language_models/test_merge_model_perplexity_mistral.py
index f08a3c1fe0..d1942ea7d4 100644
--- a/tests/saving/language_models/test_merge_model_perplexity_mistral.py
+++ b/tests/saving/language_models/test_merge_model_perplexity_mistral.py
@@ -14,8 +14,9 @@ import gc
 import sys
 from pathlib import Path
 
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
 
 from tests.utils.cleanup_utils import safe_remove_directory
 from tests.utils.perplexity_eval import ppl_model, add_to_comparison, print_model_comparison
diff --git a/tests/saving/language_models/test_merge_model_perplexity_phi_4.py b/tests/saving/language_models/test_merge_model_perplexity_phi_4.py
index df6f7ac36c..c0bd7faaf5 100644
--- a/tests/saving/language_models/test_merge_model_perplexity_phi_4.py
+++ b/tests/saving/language_models/test_merge_model_perplexity_phi_4.py
@@ -14,8 +14,10 @@ import gc
 import sys
 from pathlib import Path
 
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
+
 
 from tests.utils.cleanup_utils import safe_remove_directory
 from tests.utils.perplexity_eval import ppl_model, add_to_comparison, print_model_comparison
diff --git a/tests/saving/language_models/test_merged_model_perplexity_llama-3.1-8b.py b/tests/saving/language_models/test_merged_model_perplexity_llama-3.1-8b.py
index 5338371c04..d26771bf8c 100644
--- a/tests/saving/language_models/test_merged_model_perplexity_llama-3.1-8b.py
+++ b/tests/saving/language_models/test_merged_model_perplexity_llama-3.1-8b.py
@@ -14,8 +14,9 @@ import gc
 import sys
 from pathlib import Path
 
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
 
 from tests.utils.cleanup_utils import safe_remove_directory
 from tests.utils.perplexity_eval import ppl_model, add_to_comparison, print_model_comparison
diff --git a/tests/saving/language_models/test_merged_model_perplexity_qwen_2.5.py b/tests/saving/language_models/test_merged_model_perplexity_qwen_2.5.py
index af8e8eaae4..b80197b182 100644
--- a/tests/saving/language_models/test_merged_model_perplexity_qwen_2.5.py
+++ b/tests/saving/language_models/test_merged_model_perplexity_qwen_2.5.py
@@ -14,8 +14,9 @@ import gc
 import sys
 from pathlib import Path
 
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
 
 from tests.utils.cleanup_utils import safe_remove_directory
 from tests.utils.perplexity_eval import ppl_model, add_to_comparison, print_model_comparison
diff --git a/tests/saving/language_models/test_push_to_hub_merged.py b/tests/saving/language_models/test_push_to_hub_merged.py
index b77048905b..e1c23fa475 100644
--- a/tests/saving/language_models/test_push_to_hub_merged.py
+++ b/tests/saving/language_models/test_push_to_hub_merged.py
@@ -15,8 +15,9 @@ from huggingface_hub import HfFileSystem, hf_hub_download
 import sys
 from pathlib import Path
 
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
 
 from tests.utils.cleanup_utils import safe_remove_directory
 from tests.utils.perplexity_eval import ppl_model, add_to_comparison, print_model_comparison
diff --git a/tests/saving/language_models/test_push_to_hub_merged_sharded_index_file.py b/tests/saving/language_models/test_push_to_hub_merged_sharded_index_file.py
index 7c31520c45..04bbf2924b 100644
--- a/tests/saving/language_models/test_push_to_hub_merged_sharded_index_file.py
+++ b/tests/saving/language_models/test_push_to_hub_merged_sharded_index_file.py
@@ -15,8 +15,9 @@ from huggingface_hub import HfFileSystem, hf_hub_download
 import sys
 from pathlib import Path
 
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
 
 from tests.utils.cleanup_utils import safe_remove_directory
 from tests.utils.perplexity_eval import ppl_model, add_to_comparison, print_model_comparison
diff --git a/tests/saving/language_models/test_save_merged_grpo_model.py b/tests/saving/language_models/test_save_merged_grpo_model.py
index 35b6f65ccf..0bbb7ffd4c 100644
--- a/tests/saving/language_models/test_save_merged_grpo_model.py
+++ b/tests/saving/language_models/test_save_merged_grpo_model.py
@@ -12,8 +12,9 @@ from pathlib import Path
 import multiprocessing as mp
 import gc
 from multiprocessing import Queue
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
 
 from tests.utils.cleanup_utils import safe_remove_directory
 from tests.utils.aime_eval import evaluate_model_aime, compare_aime_results
diff --git a/tests/saving/non_peft/test_mistral_non_peft.py b/tests/saving/non_peft/test_mistral_non_peft.py
new file mode 100644
index 0000000000..7308151305
--- /dev/null
+++ b/tests/saving/non_peft/test_mistral_non_peft.py
@@ -0,0 +1,67 @@
+from unsloth import FastLanguageModel
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+from pathlib import Path
+import sys
+import warnings
+
+REPO_ROOT = Path(__file__).parents[3]
+sys.path.insert(0, str(REPO_ROOT))
+
+from tests.utils.cleanup_utils import safe_remove_directory
+
+
+print(f"\n{'='*80}")
+print("🔍 PHASE 1: Loading Base Model")
+print(f"{'='*80}")
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name="unsloth/mistral-7b-v0.3",
+        max_seq_length=2048,
+        dtype=None,
+        load_in_4bit=True,
+        load_in_8bit=False,
+        full_finetuning=False,
+    )
+
+
+print("✅ Base model loaded successfully!")
+
+### Attemtping save merge
+
+
+
+print(f"\n{'='*80}")
+print("🔍 PHASE 2: Attempting save_pretrained_merged (Should Warn)")
+print(f"{'='*80}")
+
+with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        model.save_pretrained_merged("test_output", tokenizer)
+
+        # Verify warning
+        assert len(w) >= 1, "Expected warning but none raised"
+        warning_msg = str(w[0].message)
+        expected_msg = "Model is not a PeftModel (no Lora adapters detected). Skipping Merge. Please use save_pretrained() or push_to_hub() instead!"
+        assert expected_msg in warning_msg, f"Unexpected warning: {warning_msg}"
+        assert expected_msg in warning_msg, f"Unexpected warning: {warning_msg}"
+
+print("✅ Correct warning detected for non-PeftModel merge attempt!")
+
+
+
+print(f"\n{'='*80}")
+print("🔍 PHASE 3: Using save_pretrained (Should Succeed)")
+print(f"{'='*80}")
+
+
+try:
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")  # Treat warnings as errors here
+        model.save_pretrained("test_output")
+        print("✅ Standard save_pretrained completed successfully!")
+except Exception as e:
+    assert False, f"Phase 3 failed: {e}"
+
+safe_remove_directory("./test_output")
+safe_remove_directory("./unsloth_compiled_cache")
diff --git a/tests/saving/non_peft/test_whisper_non_peft.py b/tests/saving/non_peft/test_whisper_non_peft.py
new file mode 100644
index 0000000000..40321d29b8
--- /dev/null
+++ b/tests/saving/non_peft/test_whisper_non_peft.py
@@ -0,0 +1,67 @@
+from unsloth import FastLanguageModel, FastModel
+from transformers import AutoModelForCausalLM, WhisperForConditionalGeneration
+from peft import PeftModel
+from pathlib import Path
+import sys
+import warnings
+
+REPO_ROOT = Path(__file__).parents[3]
+sys.path.insert(0, str(REPO_ROOT))
+
+from tests.utils.cleanup_utils import safe_remove_directory
+
+
+print(f"\n{'='*80}")
+print("🔍 PHASE 1: Loading Base Model")
+print(f"{'='*80}")
+
+model, tokenizer = FastModel.from_pretrained(
+    model_name = "unsloth/whisper-large-v3",
+    dtype = None, # Leave as None for auto detection
+    load_in_4bit = False, # Set to True to do 4bit quantization which reduces memory
+    auto_model = WhisperForConditionalGeneration,
+    whisper_language = "English",
+    whisper_task = "transcribe",
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+print("✅ Base model loaded successfully!")
+
+### Attemtping save merge
+
+
+
+print(f"\n{'='*80}")
+print("🔍 PHASE 2: Attempting save_pretrained_merged (Should Warn)")
+print(f"{'='*80}")
+
+with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        model.save_pretrained_merged("test_output", tokenizer)
+
+        # Verify warning
+        assert len(w) >= 1, "Expected warning but none raised"
+        warning_msg = str(w[0].message)
+        expected_msg = "Model is not a PeftModel (no Lora adapters detected). Skipping Merge. Please use save_pretrained() or push_to_hub() instead!"
+        assert expected_msg in warning_msg, f"Unexpected warning: {warning_msg}"
+        assert expected_msg in warning_msg, f"Unexpected warning: {warning_msg}"
+
+print("✅ Correct warning detected for non-PeftModel merge attempt!")
+
+
+
+print(f"\n{'='*80}")
+print("🔍 PHASE 3: Using save_pretrained (Should Succeed)")
+print(f"{'='*80}")
+
+
+try:
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")  # Treat warnings as errors here
+        model.save_pretrained("test_output")
+        print("✅ Standard save_pretrained completed successfully!")
+except Exception as e:
+    assert False, f"Phase 3 failed: {e}"
+
+safe_remove_directory("./test_output")
+safe_remove_directory("./unsloth_compiled_cache")
diff --git a/tests/saving/text_to_speech_models/test_csm.py b/tests/saving/text_to_speech_models/test_csm.py
new file mode 100644
index 0000000000..c3703cacfb
--- /dev/null
+++ b/tests/saving/text_to_speech_models/test_csm.py
@@ -0,0 +1,156 @@
+from unsloth import FastLanguageModel, FastModel
+from transformers import CsmForConditionalGeneration
+import torch
+# ruff: noqa
+import sys
+from pathlib import Path
+from peft import PeftModel
+import warnings
+import requests
+
+REPO_ROOT = Path(__file__).parents[3]
+sys.path.insert(0, str(REPO_ROOT))
+
+from tests.utils.cleanup_utils import safe_remove_directory
+from tests.utils.os_utils import require_package, require_python_package
+
+require_package("ffmpeg", "ffmpeg")
+require_python_package("soundfile")
+
+import soundfile as sf
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 1: Loading Model and LoRA Adapters")
+print(f"{'='*80}")
+
+
+model, tokenizer = FastModel.from_pretrained(
+    model_name = "unsloth/csm-1b",
+    max_seq_length= 2048, # Choose any for long context!
+    dtype = None, # Leave as None for auto-detection
+    auto_model = CsmForConditionalGeneration,
+    load_in_4bit = False, # Select True for 4bit - reduces memory usage
+)
+
+
+base_model_class = model.__class__.__name__
+
+
+model = FastModel.get_peft_model(
+    model,
+    r = 32, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 32,
+    lora_dropout = 0, # Supports any, but = 0 is optimized
+    bias = "none",    # Supports any, but = "none" is optimized
+    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+)
+
+print("✅ Model and LoRA adapters loaded successfully!")
+
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 2: Checking Model Class Type")
+print(f"{'='*80}")
+
+assert isinstance(model, PeftModel), "Model should be an instance of PeftModel"
+print("✅ Model is an instance of PeftModel!")
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 3: Checking Config Model Class Type")
+print(f"{'='*80}")
+
+def find_lora_base_model(model_to_inspect):
+    current = model_to_inspect
+    if hasattr(current, "base_model"):
+        current = current.base_model
+    if hasattr(current, "model"):
+        current = current.model
+    return current
+pass
+
+
+config_model = find_lora_base_model(model) if isinstance(model, PeftModel) else model
+
+assert config_model.__class__.__name__ == base_model_class, f"Expected config_model class to be {base_model_class}"
+print("✅ config_model returns correct Base Model class:", str(base_model_class))
+
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 4: Saving and Merging Model")
+print(f"{'='*80}")
+
+with warnings.catch_warnings():
+    warnings.simplefilter("error")  # Treat warnings as errors
+    try:
+        model.save_pretrained_merged("csm", tokenizer)
+        print("✅ Model saved and merged successfully without warnings!")
+    except Exception as e:
+        assert False, f"Model saving/merging failed with exception: {e}"
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 5: Loading Model for Inference")
+print(f"{'='*80}")
+
+
+model, processor = FastModel.from_pretrained(
+    model_name = "./csm",
+    max_seq_length= 2048, # Choose any for long context!
+    dtype = None, # Leave as None for auto-detection
+    auto_model = CsmForConditionalGeneration,
+    load_in_4bit = False, # Select True for 4bit - reduces memory usage
+)
+
+from transformers import AutoProcessor
+processor = AutoProcessor.from_pretrained("unsloth/csm-1b")
+
+print("✅ Model loaded for inference successfully!")
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 6: Running Inference")
+print(f"{'='*80}")
+
+
+from transformers import pipeline
+import torch
+output_audio_path = "csm_audio.wav"
+try:
+    text = "We just finished fine tuning a text to speech model... and it's pretty good!"
+    speaker_id = 0
+    inputs = processor(f"[{speaker_id}]{text}", add_special_tokens=True).to("cuda")
+    audio_values = model.generate(
+        **inputs,
+        max_new_tokens=125, # 125 tokens is 10 seconds of audio, for longer speech increase this
+        # play with these parameters to get the best results
+        depth_decoder_temperature=0.6,
+        depth_decoder_top_k=0,
+        depth_decoder_top_p=0.9,
+        temperature=0.8,
+        top_k=50,
+        top_p=1.0,
+        #########################################################
+        output_audio=True
+    )
+    audio = audio_values[0].to(torch.float32).cpu().numpy()
+    sf.write("example_without_context.wav", audio, 24000)
+    print(f"✅ Audio generated and saved to {output_audio_path}!")
+except Exception as e:
+    assert False, f"Inference failed with exception: {e}"
+
+
+## assert that transcribed_text contains The birch canoe slid on the smooth planks. Glued the sheet to the dark blue background. It's easy to tell the depth of a well. Four hours of steady work faced us.
+
+print("✅ All sections passed successfully!")
+
+
+safe_remove_directory("./unsloth_compiled_cache")
+safe_remove_directory("./csm")
diff --git a/tests/saving/text_to_speech_models/test_lasa.py b/tests/saving/text_to_speech_models/test_lasa.py
new file mode 100644
index 0000000000..a4bc5eda84
--- /dev/null
+++ b/tests/saving/text_to_speech_models/test_lasa.py
@@ -0,0 +1,217 @@
+from unsloth import FastLanguageModel, FastModel
+from transformers import CsmForConditionalGeneration
+import torch
+# ruff: noqa
+import sys
+from pathlib import Path
+from peft import PeftModel
+import warnings
+import requests
+
+
+REPO_ROOT = Path(__file__).parents[3]
+sys.path.insert(0, str(REPO_ROOT))
+
+
+from tests.utils.cleanup_utils import safe_remove_directory
+from tests.utils.os_utils import require_package, require_python_package
+
+require_package("ffmpeg", "ffmpeg")
+require_python_package("soundfile")
+require_python_package("xcodec2")
+
+import soundfile as sf
+from xcodec2.modeling_xcodec2 import XCodec2Model
+XCODEC2_MODEL_NAME = "HKUST-Audio/xcodec2"
+SAMPLE_RATE = 16000
+DEVICE = "cuda"
+
+try:
+    codec_model = XCodec2Model.from_pretrained(XCODEC2_MODEL_NAME)
+
+except Exception as e:
+    raise f"ERROR loading XCodec2 model: {e}."
+
+codec_model.to('cpu')
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 1: Loading Model and LoRA Adapters")
+print(f"{'='*80}")
+
+max_seq_length = 2048
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/Llasa-1B",
+    max_seq_length = max_seq_length,
+    dtype = None, # Select None for auto detection
+    load_in_4bit = False, # Choose True for 4bit which reduces memory
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+base_model_class = model.__class__.__name__
+
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 128, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = ["q_proj", "v_proj"],
+    lora_alpha = 128,
+    lora_dropout = 0, # Supports any, but = 0 is optimized
+    bias = "none",    # Supports any, but = "none" is optimized
+    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+)
+
+print("✅ Model and LoRA adapters loaded successfully!")
+
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 2: Checking Model Class Type")
+print(f"{'='*80}")
+
+assert isinstance(model, PeftModel), "Model should be an instance of PeftModel"
+print("✅ Model is an instance of PeftModel!")
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 3: Checking Config Model Class Type")
+print(f"{'='*80}")
+
+def find_lora_base_model(model_to_inspect):
+    current = model_to_inspect
+    if hasattr(current, "base_model"):
+        current = current.base_model
+    if hasattr(current, "model"):
+        current = current.model
+    return current
+pass
+
+
+config_model = find_lora_base_model(model) if isinstance(model, PeftModel) else model
+
+assert config_model.__class__.__name__ == base_model_class, f"Expected config_model class to be {base_model_class}"
+print("✅ config_model returns correct Base Model class:", str(base_model_class))
+
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 4: Saving and Merging Model")
+print(f"{'='*80}")
+
+with warnings.catch_warnings():
+    warnings.simplefilter("error")  # Treat warnings as errors
+    try:
+        model.save_pretrained_merged("lasa", tokenizer)
+        print("✅ Model saved and merged successfully without warnings!")
+    except Exception as e:
+        assert False, f"Model saving/merging failed with exception: {e}"
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 5: Loading Model for Inference")
+print(f"{'='*80}")
+
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "./lasa",
+    max_seq_length = max_seq_length,
+    dtype = None, # Select None for auto detection
+    load_in_4bit = False, # Choose True for 4bit which reduces memory
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+#from transformers import AutoProcessor
+#processor = AutoProcessor.from_pretrained("unsloth/csm-1b")
+
+print("✅ Model loaded for inference successfully!")
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 6: Running Inference")
+print(f"{'='*80}")
+
+
+from transformers import pipeline
+import torch
+output_audio_path = "lasa_audio.wav"
+input_text = "Hey there my name is Elise, <giggles> and I'm a speech generation model that can sound like a person."
+
+FastLanguageModel.for_inference(model)
+
+def ids_to_speech_tokens(speech_ids):
+
+    speech_tokens_str = []
+    for speech_id in speech_ids:
+        speech_tokens_str.append(f"<|s_{speech_id}|>")
+    return speech_tokens_str
+
+def extract_speech_ids(speech_tokens_str):
+
+    speech_ids = []
+    for token_str in speech_tokens_str:
+        if token_str.startswith('<|s_') and token_str.endswith('|>'):
+            num_str = token_str[4:-2]
+
+            num = int(num_str)
+            speech_ids.append(num)
+        else:
+            print(f"Unexpected token: {token_str}")
+    return speech_ids
+
+#TTS start!
+with torch.inference_mode():
+    with torch.amp.autocast('cuda',dtype=model.dtype):
+        formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"
+
+        # Tokenize the text
+        chat = [
+            {"role": "user", "content": "Convert the text to speech:" + formatted_text},
+            {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
+        ]
+
+        input_ids = tokenizer.apply_chat_template(
+            chat,
+            tokenize=True,
+            return_tensors='pt',
+            continue_final_message=True
+        )
+        input_ids = input_ids.to('cuda')
+
+        speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')
+
+        # Generate the speech autoregressively
+        outputs = model.generate(
+            input_ids,
+            max_length=2048,  # We trained our model with a max length of 2048
+            eos_token_id= speech_end_id ,
+            do_sample=True,
+            top_p=1.2,           #  Adjusts the diversity of generated content
+            temperature=1.2,   #  Controls randomness in output
+        )
+    # Extract the speech tokens
+    generated_ids = outputs[0][input_ids.shape[1]:-1]
+
+    speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+
+    # Convert  token <|s_23456|> to int 23456
+    speech_tokens = extract_speech_ids(speech_tokens)
+
+    speech_tokens = torch.tensor(speech_tokens).cpu().unsqueeze(0).unsqueeze(0)
+
+    # Decode the speech tokens to speech waveform
+    gen_wav = codec_model.decode_code(speech_tokens)
+try:
+    sf.write(output_audio_path, gen_wav[0, 0, :].cpu().numpy(), 16000)
+except Exception as e:
+    assert False, f"Inference failed with exception: {e}"
+
+
+## assert that transcribed_text contains The birch canoe slid on the smooth planks. Glued the sheet to the dark blue background. It's easy to tell the depth of a well. Four hours of steady work faced us.
+
+print("✅ All sections passed successfully!")
+
+
+safe_remove_directory("./unsloth_compiled_cache")
+safe_remove_directory("./lasa")
diff --git a/tests/saving/text_to_speech_models/test_orpheus.py b/tests/saving/text_to_speech_models/test_orpheus.py
new file mode 100644
index 0000000000..813a80aac4
--- /dev/null
+++ b/tests/saving/text_to_speech_models/test_orpheus.py
@@ -0,0 +1,254 @@
+from unsloth import FastLanguageModel, FastModel
+from transformers import CsmForConditionalGeneration
+import torch
+# ruff: noqa
+import sys
+from pathlib import Path
+from peft import PeftModel
+import warnings
+import requests
+
+REPO_ROOT = Path(__file__).parents[3]
+sys.path.insert(0, str(REPO_ROOT))
+
+from tests.utils.cleanup_utils import safe_remove_directory
+from tests.utils.os_utils import require_package, require_python_package
+
+require_package("ffmpeg", "ffmpeg")
+require_python_package("soundfile")
+require_python_package("snac")
+
+import soundfile as sf
+from snac import SNAC
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+snac_model = snac_model.to("cuda")
+print(f"\n{'='*80}")
+print("🔍 SECTION 1: Loading Model and LoRA Adapters")
+print(f"{'='*80}")
+
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/orpheus-3b-0.1-ft",
+    max_seq_length= 2048, # Choose any for long context!
+    dtype = None, # Select None for auto detection
+    load_in_4bit = False, # Select True for 4bit which reduces memory usage
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+base_model_class = model.__class__.__name__
+
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 64, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 64,
+    lora_dropout = 0, # Supports any, but = 0 is optimized
+    bias = "none",    # Supports any, but = "none" is optimized
+    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+)
+print("✅ Model and LoRA adapters loaded successfully!")
+
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 2: Checking Model Class Type")
+print(f"{'='*80}")
+
+assert isinstance(model, PeftModel), "Model should be an instance of PeftModel"
+print("✅ Model is an instance of PeftModel!")
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 3: Checking Config Model Class Type")
+print(f"{'='*80}")
+
+def find_lora_base_model(model_to_inspect):
+    current = model_to_inspect
+    if hasattr(current, "base_model"):
+        current = current.base_model
+    if hasattr(current, "model"):
+        current = current.model
+    return current
+pass
+
+
+config_model = find_lora_base_model(model) if isinstance(model, PeftModel) else model
+
+assert config_model.__class__.__name__ == base_model_class, f"Expected config_model class to be {base_model_class}"
+print("✅ config_model returns correct Base Model class:", str(base_model_class))
+
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 4: Saving and Merging Model")
+print(f"{'='*80}")
+
+with warnings.catch_warnings():
+    warnings.simplefilter("error")  # Treat warnings as errors
+    try:
+        model.save_pretrained_merged("orpheus", tokenizer)
+        print("✅ Model saved and merged successfully without warnings!")
+    except Exception as e:
+        assert False, f"Model saving/merging failed with exception: {e}"
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 5: Loading Model for Inference")
+print(f"{'='*80}")
+
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/orpheus-3b-0.1-ft",
+    max_seq_length= 2048, # Choose any for long context!
+    dtype = None, # Select None for auto detection
+    load_in_4bit = False, # Select True for 4bit which reduces memory usage
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+#from transformers import AutoProcessor
+#processor = AutoProcessor.from_pretrained("unsloth/csm-1b")
+
+print("✅ Model loaded for inference successfully!")
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 6: Running Inference")
+print(f"{'='*80}")
+
+
+#@title Run Inference
+
+
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+# Moving snac_model cuda to cpu
+snac_model.to("cpu")
+prompts = [
+    "Hey there my name is Elise, <giggles> and I'm a speech generation model that can sound like a person.",
+]
+
+chosen_voice = None # None for single-speaker
+
+prompts_ = [(f"{chosen_voice}: " + p) if chosen_voice else p for p in prompts]
+
+all_input_ids = []
+
+for prompt in prompts_:
+  input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+  all_input_ids.append(input_ids)
+
+start_token = torch.tensor([[ 128259]], dtype=torch.int64) # Start of human
+end_tokens = torch.tensor([[128009, 128260]], dtype=torch.int64) # End of text, End of human
+
+all_modified_input_ids = []
+for input_ids in all_input_ids:
+  modified_input_ids = torch.cat([start_token, input_ids, end_tokens], dim=1) # SOH SOT Text EOT EOH
+  all_modified_input_ids.append(modified_input_ids)
+
+all_padded_tensors = []
+all_attention_masks = []
+max_length = max([modified_input_ids.shape[1] for modified_input_ids in all_modified_input_ids])
+for modified_input_ids in all_modified_input_ids:
+  padding = max_length - modified_input_ids.shape[1]
+  padded_tensor = torch.cat([torch.full((1, padding), 128263, dtype=torch.int64), modified_input_ids], dim=1)
+  attention_mask = torch.cat([torch.zeros((1, padding), dtype=torch.int64), torch.ones((1, modified_input_ids.shape[1]), dtype=torch.int64)], dim=1)
+  all_padded_tensors.append(padded_tensor)
+  all_attention_masks.append(attention_mask)
+
+all_padded_tensors = torch.cat(all_padded_tensors, dim=0)
+all_attention_masks = torch.cat(all_attention_masks, dim=0)
+
+input_ids = all_padded_tensors.to("cuda")
+attention_mask = all_attention_masks.to("cuda")
+generated_ids = model.generate(
+      input_ids=input_ids,
+      attention_mask=attention_mask,
+      max_new_tokens=1200,
+      do_sample=True,
+      temperature=0.6,
+      top_p=0.95,
+      repetition_penalty=1.1,
+      num_return_sequences=1,
+      eos_token_id=128258,
+     use_cache = True
+  )
+token_to_find = 128257
+token_to_remove = 128258
+
+token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)
+
+if len(token_indices[1]) > 0:
+    last_occurrence_idx = token_indices[1][-1].item()
+    cropped_tensor = generated_ids[:, last_occurrence_idx+1:]
+else:
+    cropped_tensor = generated_ids
+
+mask = cropped_tensor != token_to_remove
+
+processed_rows = []
+
+for row in cropped_tensor:
+    masked_row = row[row != token_to_remove]
+    processed_rows.append(masked_row)
+
+code_lists = []
+
+for row in processed_rows:
+    row_length = row.size(0)
+    new_length = (row_length // 7) * 7
+    trimmed_row = row[:new_length]
+    trimmed_row = [t - 128266 for t in trimmed_row]
+    code_lists.append(trimmed_row)
+
+
+def redistribute_codes(code_list):
+  layer_1 = []
+  layer_2 = []
+  layer_3 = []
+  for i in range((len(code_list)+1)//7):
+    layer_1.append(code_list[7*i])
+    layer_2.append(code_list[7*i+1]-4096)
+    layer_3.append(code_list[7*i+2]-(2*4096))
+    layer_3.append(code_list[7*i+3]-(3*4096))
+    layer_2.append(code_list[7*i+4]-(4*4096))
+    layer_3.append(code_list[7*i+5]-(5*4096))
+    layer_3.append(code_list[7*i+6]-(6*4096))
+  codes = [torch.tensor(layer_1).unsqueeze(0),
+         torch.tensor(layer_2).unsqueeze(0),
+         torch.tensor(layer_3).unsqueeze(0)]
+
+  # codes = [c.to("cuda") for c in codes]
+  audio_hat = snac_model.decode(codes)
+  return audio_hat
+
+my_samples = []
+for code_list in code_lists:
+  samples = redistribute_codes(code_list)
+  my_samples.append(samples)
+output_path = "orpheus_audio.wav"
+try:
+    for i, samples in enumerate(my_samples):
+        audio_data = samples.detach().squeeze().cpu().numpy()
+        import soundfile as sf
+        sf.write(output_path, audio_data, 24000)  # Explicitly pass sample rate
+        print(f"✅ Audio saved to {output_path}!")
+except Exception as e:
+    assert False, f"Inference failed with exception: {e}"
+
+# Verify the file exists
+import os
+assert os.path.exists(output_path), f"Audio file not found at {output_path}"
+print("✅ Audio file exists on disk!")
+del my_samples, samples
+## assert that transcribed_text contains The birch canoe slid on the smooth planks. Glued the sheet to the dark blue background. It's easy to tell the depth of a well. Four hours of steady work faced us.
+
+print("✅ All sections passed successfully!")
+
+
+safe_remove_directory("./unsloth_compiled_cache")
+safe_remove_directory("./orpheus")
diff --git a/tests/saving/text_to_speech_models/test_whisper.py b/tests/saving/text_to_speech_models/test_whisper.py
new file mode 100644
index 0000000000..f29213c3a7
--- /dev/null
+++ b/tests/saving/text_to_speech_models/test_whisper.py
@@ -0,0 +1,189 @@
+from unsloth import FastLanguageModel, FastModel
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+import torch
+# ruff: noqa
+import sys
+from pathlib import Path
+from peft import PeftModel
+import warnings
+import requests
+
+
+REPO_ROOT = Path(__file__).parents[3]
+sys.path.insert(0, str(REPO_ROOT))
+
+
+from tests.utils.cleanup_utils import safe_remove_directory
+from tests.utils.os_utils import require_package, require_python_package
+
+require_package("ffmpeg", "ffmpeg")
+require_python_package("soundfile")
+
+import soundfile as sf
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 1: Loading Model and LoRA Adapters")
+print(f"{'='*80}")
+
+
+model, tokenizer = FastModel.from_pretrained(
+    model_name = "unsloth/whisper-large-v3",
+    dtype = None, # Leave as None for auto detection
+    load_in_4bit = False, # Set to True to do 4bit quantization which reduces memory
+    auto_model = WhisperForConditionalGeneration,
+    whisper_language = "English",
+    whisper_task = "transcribe",
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+
+base_model_class = model.__class__.__name__
+#https://github.com/huggingface/transformers/issues/37172
+model.generation_config.input_ids = model.generation_config.forced_decoder_ids
+model.generation_config.forced_decoder_ids = None
+
+
+model = FastModel.get_peft_model(
+    model,
+    r = 64, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    target_modules = ["q_proj", "v_proj"],
+    lora_alpha = 64,
+    lora_dropout = 0, # Supports any, but = 0 is optimized
+    bias = "none",    # Supports any, but = "none" is optimized
+    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+    task_type = None, # ** MUST set this for Whisper **
+)
+
+print("✅ Model and LoRA adapters loaded successfully!")
+
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 2: Checking Model Class Type")
+print(f"{'='*80}")
+
+assert isinstance(model, PeftModel), "Model should be an instance of PeftModel"
+print("✅ Model is an instance of PeftModel!")
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 3: Checking Config Model Class Type")
+print(f"{'='*80}")
+
+def find_lora_base_model(model_to_inspect):
+    current = model_to_inspect
+    if hasattr(current, "base_model"):
+        current = current.base_model
+    if hasattr(current, "model"):
+        current = current.model
+    return current
+pass
+
+
+config_model = find_lora_base_model(model) if isinstance(model, PeftModel) else model
+
+assert config_model.__class__.__name__ == base_model_class, f"Expected config_model class to be {base_model_class}"
+print("✅ config_model returns correct Base Model class:", str(base_model_class))
+
+
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 4: Saving and Merging Model")
+print(f"{'='*80}")
+
+with warnings.catch_warnings():
+    warnings.simplefilter("error")  # Treat warnings as errors
+    try:
+        model.save_pretrained_merged("whisper", tokenizer)
+        print("✅ Model saved and merged successfully without warnings!")
+    except Exception as e:
+        assert False, f"Model saving/merging failed with exception: {e}"
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 5: Loading Model for Inference")
+print(f"{'='*80}")
+
+
+model, tokenizer = FastModel.from_pretrained(
+    model_name = "./whisper",
+    dtype = None, # Leave as None for auto detection
+    load_in_4bit = False, # Set to True to do 4bit quantization which reduces memory
+    auto_model = WhisperForConditionalGeneration,
+    whisper_language = "English",
+    whisper_task = "transcribe",
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+# model = WhisperForConditionalGeneration.from_pretrained("./whisper")
+# processor = WhisperProcessor.from_pretrained("./whisper")
+
+print("✅ Model loaded for inference successfully!")
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 6: Downloading Sample Audio File")
+print(f"{'='*80}")
+
+audio_url = "https://upload.wikimedia.org/wikipedia/commons/5/5b/Speech_12dB_s16.flac"
+audio_file = "Speech_12dB_s16.flac"
+
+try:
+    headers = {
+        "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+    }
+    response = requests.get(audio_url, headers=headers)
+    response.raise_for_status()
+    with open(audio_file, "wb") as f:
+        f.write(response.content)
+    print("✅ Audio file downloaded successfully!")
+except Exception as e:
+    assert False, f"Failed to download audio file: {e}"
+
+print(f"\n{'='*80}")
+print("🔍 SECTION 7: Running Inference")
+print(f"{'='*80}")
+
+
+from transformers import pipeline
+import torch
+FastModel.for_inference(model)
+model.eval()
+#Create pipeline without specifying the device
+whisper = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=tokenizer.tokenizer,
+    feature_extractor=tokenizer.feature_extractor,
+    processor=tokenizer,
+    return_language=True,
+    torch_dtype=torch.float16  # Remove the device parameter
+)
+# Example usage
+audio_file = "Speech_12dB_s16.flac"
+transcribed_text = whisper(audio_file)
+# audio, sr = sf.read(audio_file)
+# input_features = processor(audio, return_tensors="pt").input_features
+# transcribed_text = model.generate(input_features=input_features)
+print(f"📝 Transcribed Text: {transcribed_text['text']}")
+
+## assert that transcribed_text contains The birch canoe slid on the smooth planks. Glued the sheet to the dark blue background. It's easy to tell the depth of a well. Four hours of steady work faced us.
+
+expected_phrases = [
+    "birch canoe slid on the smooth planks",
+    "sheet to the dark blue background",
+    "easy to tell the depth of a well",
+    "Four hours of steady work faced us",
+]
+
+transcribed_lower = transcribed_text["text"].lower()
+all_phrases_found = all(phrase.lower() in transcribed_lower for phrase in expected_phrases)
+
+assert all_phrases_found, f"Expected phrases not found in transcription: {transcribed_text['text']}"
+print("✅ Transcription contains all expected phrases!")
+
+
+safe_remove_directory("./unsloth_compiled_cache")
+safe_remove_directory("./whisper")
diff --git a/tests/saving/vision_models/test_index_file_sharded_model.py b/tests/saving/vision_models/test_index_file_sharded_model.py
index 71dfe24362..09c4bb19e1 100644
--- a/tests/saving/vision_models/test_index_file_sharded_model.py
+++ b/tests/saving/vision_models/test_index_file_sharded_model.py
@@ -11,8 +11,9 @@ from huggingface_hub import HfFileSystem
 import sys
 from pathlib import Path
 
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
 
 from tests.utils.cleanup_utils import safe_remove_directory
 
diff --git a/tests/saving/vision_models/test_push_to_hub_merged.py b/tests/saving/vision_models/test_push_to_hub_merged.py
index 9845c3e1d0..372d225732 100644
--- a/tests/saving/vision_models/test_push_to_hub_merged.py
+++ b/tests/saving/vision_models/test_push_to_hub_merged.py
@@ -11,8 +11,10 @@ from trl import SFTTrainer, SFTConfig
 import sys
 from pathlib import Path
 
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
+
 
 from tests.utils.cleanup_utils import safe_remove_directory
 
diff --git a/tests/saving/vision_models/test_save_merge_vision_model_ocr_benchmark.py b/tests/saving/vision_models/test_save_merge_vision_model_ocr_benchmark.py
index 8f2617e556..e556f62465 100644
--- a/tests/saving/vision_models/test_save_merge_vision_model_ocr_benchmark.py
+++ b/tests/saving/vision_models/test_save_merge_vision_model_ocr_benchmark.py
@@ -11,8 +11,9 @@ from trl import SFTTrainer, SFTConfig
 import sys
 from pathlib import Path
 
+
 REPO_ROOT = Path(__file__).parents[3]
-sys.path.append(str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT))
 
 from tests.utils.cleanup_utils import safe_remove_directory
 from tests.utils.ocr_eval import OCRModelEvaluator
diff --git a/tests/utils/os_utils.py b/tests/utils/os_utils.py
new file mode 100644
index 0000000000..281fcdbaf5
--- /dev/null
+++ b/tests/utils/os_utils.py
@@ -0,0 +1,119 @@
+import subprocess
+import sys
+import os
+import shutil
+import importlib
+
+def detect_package_manager():
+    """Detect the available package manager"""
+    package_managers = {
+        'apt': '/usr/bin/apt',
+        'yum': '/usr/bin/yum',
+        'dnf': '/usr/bin/dnf',
+        'pacman': '/usr/bin/pacman',
+        'zypper': '/usr/bin/zypper'
+    }
+
+    for pm, path in package_managers.items():
+        if os.path.exists(path):
+            return pm
+    return None
+
+def check_package_installed(package_name, package_manager=None):
+    """Check if a package is installed using the system package manager"""
+
+    if package_manager is None:
+        package_manager = detect_package_manager()
+
+    if package_manager is None:
+        print("Warning: Could not detect package manager")
+        return None
+
+    try:
+        if package_manager == 'apt':
+            # Check with dpkg
+            result = subprocess.run(['dpkg', '-l', package_name],
+                                  capture_output=True, text=True)
+            return result.returncode == 0
+
+        elif package_manager in ['yum', 'dnf']:
+            # Check with rpm
+            result = subprocess.run(['rpm', '-q', package_name],
+                                  capture_output=True, text=True)
+            return result.returncode == 0
+
+        elif package_manager == 'pacman':
+            result = subprocess.run(['pacman', '-Q', package_name],
+                                  capture_output=True, text=True)
+            return result.returncode == 0
+
+        elif package_manager == 'zypper':
+            result = subprocess.run(['zypper', 'se', '-i', package_name],
+                                  capture_output=True, text=True)
+            return package_name in result.stdout
+
+    except Exception as e:
+        print(f"Error checking package: {e}")
+        return None
+
+def require_package(package_name, executable_name=None):
+    """Require a package to be installed, exit if not found"""
+
+    # First check if executable is in PATH (most reliable)
+    if executable_name:
+        if shutil.which(executable_name):
+            print(f"✓ {executable_name} is available")
+            return
+
+    # Then check with package manager
+    pm = detect_package_manager()
+    is_installed = check_package_installed(package_name, pm)
+
+    if is_installed:
+        print(f"✓ Package {package_name} is installed")
+        return
+
+    # Package not found - show installation instructions
+    print(f"❌ Error: {package_name} is not installed")
+    print(f"\nPlease install {package_name} using your system package manager:")
+
+    install_commands = {
+        'apt': f"sudo apt update && sudo apt install {package_name}",
+        'yum': f"sudo yum install {package_name}",
+        'dnf': f"sudo dnf install {package_name}",
+        'pacman': f"sudo pacman -S {package_name}",
+        'zypper': f"sudo zypper install {package_name}"
+    }
+
+    if pm and pm in install_commands:
+        print(f"  {install_commands[pm]}")
+    else:
+        for pm_name, cmd in install_commands.items():
+            print(f"  {pm_name}: {cmd}")
+
+    print(f"\nAlternatively, install with conda:")
+    print(f"  conda install -c conda-forge {package_name}")
+
+    print(f"\nPlease install the required package and run the script again.")
+    sys.exit(1)
+
+# Usage
+#require_package("ffmpeg", "ffmpeg")
+
+def require_python_package(package_name, import_name=None, pip_name=None):
+    """Require a Python package to be installed, exit if not found"""
+    if import_name is None:
+        import_name = package_name
+    if pip_name is None:
+        pip_name = package_name
+
+    if importlib.util.find_spec(import_name) is None:
+        print(f"❌ Error: Python package '{package_name}' is not installed")
+        print(f"\nPlease install {package_name} using pip:")
+        print(f"  pip install {pip_name}")
+        print(f"  # or with conda:")
+        print(f"  conda install {pip_name}")
+        print(f"\nAfter installation, run this script again.")
+        sys.exit(1)
+    else:
+        print(f"✓ Python package '{package_name}' is installed")