a6dc10dad2* Reduce and tighten comments and docstrings in tests Shorten verbose comments and docstrings across the test suite without changing any test logic. Remove narration that restates the next line, collapse long module and test docstrings to a single line, and drop banner separators. Keep regression context (issue and PR references, run ids), skip reasons, mocking and timing rationale, license headers, lint and type directives, and commented-out code. Comments and docstrings only: an AST signature check confirms no code, assertions, or string literals changed, and the suite byte-compiles cleanly. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
from unsloth import FastLanguageModel, FastModel
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from transformers import AutoModelForCausalLM, WhisperForConditionalGeneration
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from peft import PeftModel
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from pathlib import Path
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import sys
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import warnings
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REPO_ROOT = Path(__file__).parents[3]
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sys.path.insert(0, str(REPO_ROOT))
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from tests.utils.cleanup_utils import safe_remove_directory
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print(f"\n{'='*80}")
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print("🔍 PHASE 1: Loading Base Model")
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print(f"{'='*80}")
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model, tokenizer = FastModel.from_pretrained(
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model_name = "unsloth/whisper-large-v3",
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dtype = None, # Leave as None for auto detection
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load_in_4bit = False, # Set to True to do 4bit quantization which reduces memory
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auto_model = WhisperForConditionalGeneration,
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whisper_language = "English",
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whisper_task = "transcribe",
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# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
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)
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print("✅ Base model loaded successfully!")
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print(f"\n{'='*80}")
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print("🔍 PHASE 2: Attempting save_pretrained_merged (Should Warn)")
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print(f"{'='*80}")
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with warnings.catch_warnings(record = True) as w:
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warnings.simplefilter("always")
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model.save_pretrained_merged("test_output", tokenizer)
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assert len(w) >= 1, "Expected warning but none raised"
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warning_msg = str(w[0].message)
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expected_msg = "Model is not a PeftModel (no Lora adapters detected). Skipping Merge. Please use save_pretrained() or push_to_hub() instead!"
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assert expected_msg in warning_msg, f"Unexpected warning: {warning_msg}"
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assert expected_msg in warning_msg, f"Unexpected warning: {warning_msg}"
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print("✅ Correct warning detected for non-PeftModel merge attempt!")
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print(f"\n{'='*80}")
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print("🔍 PHASE 3: Using save_pretrained (Should Succeed)")
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print(f"{'='*80}")
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try:
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with warnings.catch_warnings():
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warnings.simplefilter("error") # any warning -> failure
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model.save_pretrained("test_output")
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print("✅ Standard save_pretrained completed successfully!")
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except Exception as e:
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assert False, f"Phase 3 failed: {e}"
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safe_remove_directory("./test_output")
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safe_remove_directory("./unsloth_compiled_cache")
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