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ydshieh
commented
Aug 27, 2025
| out = model(input_ids).logits.float().cpu() | ||
| # Expected mean on dim = -1 | ||
| EXPECTED_MEAN = torch.tensor([[-4.2125, -3.6416, -4.9136, -4.3005, -4.9938, -3.4393, -3.5195, -4.1621]]) | ||
| EXPECTED_MEAN = torch.tensor([[-4.2106, -3.6411, -4.9111, -4.2840, -4.9950, -3.4438, -3.5262, -4.1624]]) |
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changed because now we use fp16 (previously fp32)
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[For maintainers] Suggested jobs to run (before merge) run-slow: qwen2_moe |
ydshieh
commented
Aug 27, 2025
| def test_speculative_generation(self): | ||
| EXPECTED_TEXT_COMPLETION = ( | ||
| "To be or not to be, that is the question.\nThe answer is to be, of course. But what does it" | ||
| "To be or not to be, that is the question. Whether 'tis nobler in the mind to suffer the sl" |
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the previous value never pass
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
zucchini-nlp
approved these changes
Aug 27, 2025
| @@ -206,21 +219,12 @@ def test_model_a2_7b_long_prompt(self): | |||
| generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) | |||
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another test where assistant_model is not actually used 😄
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What does this PR do?
The CI job of the tests in
Qwen2MoeIntegrationTestis killed from time to time due to the CPU memory limit (60 G), which is unclear to me why the usage may differ in different runs.This PR simply reuses the same model set once in the first test, except the
test_model_a2_7b_long_prompt_flash_attn, because it loads the model usingflash_attn.