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Changing the test model in Quanto kv cache #36670

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Merged
MekkCyber merged 2 commits into from
Mar 13, 2025
Merged

Changing the test model in Quanto kv cache #36670

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d3c0dea
changing model
MekkCyber Mar 12, 2025
a5b8ae0
Merge branch 'main' into fixing_quanto_kv_cache
MekkCyber Mar 13, 2025
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10 changes: 6 additions & 4 deletions tests/quantization/quanto_integration/test_quanto.py
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Original file line number Diff line number Diff line change
Expand Up @@ -448,17 +448,19 @@ class QuantoKVCacheQuantizationTest(unittest.TestCase):
@require_read_token
def test_quantized_cache(self):
EXPECTED_TEXT_COMPLETION = [
"Simply put, the theory of relativity states that 1) the speed of light is the same for all observers, and 2) the laws of physics are the same for all observers.\nThe first part of the theory is the most",
"My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p",
"Simply put, the theory of relativity states that 1) time and space are not absolute, but are relative to the observer, and 2) the laws of physics are the same everywhere in the universe. This means that the speed of light is",
"My favorite all time favorite condiment is ketchup. I love how it adds a sweet and tangy flavor to my food. I also enjoy using it as a dip for fries, burgers, and grilled meats. It's a classic condiment that never",
]

prompts = [
"Simply put, the theory of relativity states that ",
"My favorite all time favorite condiment is ketchup.",
]
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf", pad_token="</s>", padding_side="left")
tokenizer = LlamaTokenizer.from_pretrained(
"unsloth/Llama-3.2-1B-Instruct", pad_token="</s>", padding_side="left"
)
model = LlamaForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf", device_map="sequential", torch_dtype=torch.float16
"unsloth/Llama-3.2-1B-Instruct", device_map="sequential", torch_dtype=torch.float16
)
inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(torch_device)

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