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Update README.md

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@@ -174,27 +174,6 @@ output_text = tokenizer.batch_decode(
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  generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
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  )
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  print("Response:", output_text[0][len(prompt):])
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-
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- # Local Benchmark
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- import torch.utils.benchmark as benchmark
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- from torchao.utils import benchmark_model
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- import torchao
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-
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- def benchmark_fn(f, *args, **kwargs):
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- # Manual warmup
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- for _ in range(2):
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- f(*args, **kwargs)
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-
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- t0 = benchmark.Timer(
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- stmt="f(*args, **kwargs)",
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- globals={"args": args, "kwargs": kwargs, "f": f},
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- num_threads=torch.get_num_threads(),
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- )
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- return f"{(t0.blocked_autorange().mean):.3f}"
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-
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- torchao.quantization.utils.recommended_inductor_config_setter()
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- quantized_model = torch.compile(quantized_model, mode="max-autotune")
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- print(f"{save_to} model:", benchmark_fn(quantized_model.generate, **inputs, max_new_tokens=128))
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  ```
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  # Model Quality
 
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  generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
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  )
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  print("Response:", output_text[0][len(prompt):])
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  # Model Quality