upload the epoch two model
Browse files- README.md +4 -24
- pytorch_model-00001-of-00003.bin +1 -1
- pytorch_model-00002-of-00003.bin +1 -1
- pytorch_model-00003-of-00003.bin +1 -1
README.md
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## Examples
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````
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Sure! Here’s an example implementation of the Fibonacci sequence using memoization in Python:
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def fibonacci(n):
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"""Return the first n numbers of the Fibonacci sequence."""
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cache = {} # Memoize the previous results for faster lookups
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def memoized_fibonacci(*args, **kwargs):
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if args[0] in cache:
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return cache[args[0]]
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else:
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result = fibonacci(args[0])
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cache[args[0]] = result
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return result
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def fibonacci_recursive(n):
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"""Recursive implementation of the Fibonacci sequence."""
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if n <= 1:
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return n
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else:
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return fibonacci_recursive(n-1) + fibonacci_recursive(n-2)
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return memoized_fibonacci(*[n] * len(cache))
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In this implementation, we define a function memoized_fibonacci that takes the same arguments as fibonacci, but also receives a cache dictionary. If an argument is found in the cache dictionary, it returns the corresponding value from the cache. Otherwise, it computes the value of fibonacci(n) and stores it
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````
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```
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### Instruction: Finish the joke, a mechanic and a car salesman walk into a bar...
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### Assistant:
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```
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## Examples
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````
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### Instruction: write Python code that returns the first n numbers of the Fibonacci sequence using memoization.
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### Assistant:
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````
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```
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### Instruction: Finish the joke, a mechanic and a car salesman walk into a bar...
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### Assistant:
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```
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pytorch_model-00001-of-00003.bin
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pytorch_model-00002-of-00003.bin
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pytorch_model-00003-of-00003.bin
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