AI語言模型的演變
已設定了新的標準，尤其是在編碼和編程環境中。領導電荷為> deepSeek-v3，gpt-4o 和
結論>
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模型體系結構和設計> deepSeek-v3
deepSeek -v3是具有6710億參數的外源外源混合物（MOE）模型，每個令牌激活了370億個參數。它利用了14.8萬億代幣訓練的最先進的負載平衡和多token預測方法。該模型在多個基準測試中實現頂級性能，維持培訓效率，成本僅為278.8萬h800 gpu小時。 DeepSeek-v3 deepseek-r1 lite中的推理能力，並提供了128K上下文窗口。此外，它可以處理多種輸入類型，包括文本，結構化數據和復雜的多模式輸入，使其用於多種用例。 也請閱讀：使用DeepSeek-V3 構建AI應用程序 > gpt-4o綠色3.3 70B
METAllama3.3 70 B多語言大語言模型（LLM）是一種開源，預先培訓的，指令調節的生成模型，具有700億個參數。它旨在優化效率和可擴展性。它採用尖端技術來處理各種各樣的任務，對超過15萬億代幣進行了培訓。 Llama 3.3 70B是一種使用優化的變壓器體系結構的自動回歸語言模型。該模型在幾個基准上實現了出色的性能，並通過優化的資源分配保持培訓成本最低。
llama 3.3 70b支持寬闊的上下文窗口，並包含了高級推理功能，以實現細微和精確的任務處理。它旨在處理基於文本的輸入，但也可以處理結構化數據，在各種應用程序中提供靈活性。> DeepSeek-V3 vs GPT-4O vs Llama 3.3 70b：模型評估
1。模型概述
| Benchmark | Description | DeepSeek-V3 | GPT-4o | Llama 3.3 70B |
| MMLU | Massive Multitask Language Understanding- Test knowledge across 57 subjects including maths, history, law and more | 88.5% | 88.7% | 88.5% |
| MMLU-Pro | A more robust MMLU benchmark with more complex reasoning focused questions and reduced prompt sensitivity | 75.9% | 74.68% | 75.9% |
| MMMU | Massive Multitask Multimodal Understanding: Text understanding across text, audio,images and videos | Not available | 69.1% | Not available |
| HellaSwag | A challenging sentence completion benchmark | 88.9% | Not available | Not available |
| HumanEval | Evaluates code generation and problem solving capabilities | 82.6% | 90.2% | 88.4% |
| MATH | Tests Mathematical problem solving abilities across various difficulty levels | 61.6% | 75.9% | 77% |
| GPQA | Test PhD-level knowledge in physics, chemistry and biology that require domain expertise | 59.1% | 53.6% | 50.5% |
| IFEval | Test model’s ability to accurately follow explicit formatting instructions, generate appropriate outputs and maintain consistent instructions | 86.1% | Not available | 92.1% |
>您可以在此處找到其單獨的基準測試的結果：
談到定價，與DeepSeek-v3相比，GPT-4O的輸入和輸出令牌貴大約30倍。同時，與DeepSeek-V3有關輸入和輸出令牌的Llama 3.3 70B指令大約貴1.5倍。 在需要結構化任務完成的基準中，
DeepSeek-V3在基準中擅長，例如MMLU（大量的多任務語言理解）和HumaneVal（代碼生成）。但是，它在數學（數學解決問題）等基準中面臨挑戰，在數學（數學解決問題）中，其表現不那麼競爭。它也導致GPQA（廣義段落問題回答），表現優於該域中的其他模型。> 在HumaneVal和MMLU中，
gpt-4O表現特別出色，在那裡它以其在各種主題上產生高質量響應的能力而脫穎而出。儘管它在基於推理的任務中閃耀，但它在其他模型等專業領域（例如Math和GPQA）中掙扎。 Llama 3.3 70b在數學和IFEVAL等基準（評估後的指令）等基準中顯示出其優勢。它比其他模型更有效地展示了出色的問題解決能力，並更有效地遵循複雜的指示。它在HumaneVal和GPQA等其他基準測試中的性能不如DeepSeek-V3和GPT-4O。 > deepSeek-v3 vs gpt-4o vs llama 3.3 70b：編碼功能
現在，我們將比較DeepSeek-V3，GPT-4O和Llama 3.3 70B的代碼編寫功能。為此，我們將在所有三種模型上給出相同的提示，並在以下指標上評估他們的回答：
可讀性
q。寫python代碼以查找大量的階乘。
GPT-4O響應
使用數組（手動處理乘法和攜帶操作）來計算大數的階乘，這是一個python實現：
生成代碼的輸出
的響應駱駝3.3 70B
>
def multiply(arr, x):
"""
Multiplies an array representation of a number with an integer x.
: param arr: List of integers representing the digits of the number.
: param x: Integer to multiply with.
:return: Updated array with the result.
"""
carry = 0 # Initialize carry
for i in range(len(arr)):
prod = arr[i] * x + carry
arr[i] = prod % 10 # Store last digit of the product in the array
carry = prod // 10 # Update the carry
# Process carry, add digits to the array
while carry > 0:
arr.append(carry % 10)
carry //= 10
return arr
def factorial_large_number_array(n):
"""
Calculate the factorial of a large number using array representation.
: param n: Integer for which the factorial is to be computed.
:return: Factorial of n as a string.
"""
if n < 0:
return "Factorial is not defined for negative numbers."
if n == 0 or n == 1:
return "1"
arr = [1] # Initialize the array with the first factorial value (1)
for x in range(2, n + 1):
arr = multiply(arr, x)
# The result is stored in reverse order in the array
return ''.join(map(str, reversed(arr)))
# Input a large number
try:
number = int(input("Enter a large number to calculate its factorial: "))
result = factorial_large_number_array(number)
print(f"The factorial of {number} is:\n{result}")
except ValueError:
print("Invalid input! Please enter a valid integer.")def multiply(arr, x):
"""
Multiplies an array representation of a number with an integer x.
: param arr: List of integers representing the digits of the number.
: param x: Integer to multiply with.
:return: Updated array with the result.
"""
carry = 0 # Initialize carry
for i in range(len(arr)):
prod = arr[i] * x + carry
arr[i] = prod % 10 # Store last digit of the product in the array
carry = prod // 10 # Update the carry
# Process carry, add digits to the array
while carry > 0:
arr.append(carry % 10)
carry //= 10
return arr
def factorial_large_number_array(n):
"""
Calculate the factorial of a large number using array representation.
: param n: Integer for which the factorial is to be computed.
:return: Factorial of n as a string.
"""
if n < 0:
return "Factorial is not defined for negative numbers."
if n == 0 or n == 1:
return "1"
arr = [1] # Initialize the array with the first factorial value (1)
for x in range(2, n + 1):
arr = multiply(arr, x)
# The result is stored in reverse order in the array
return ''.join(map(str, reversed(arr)))
# Input a large number
try:
number = int(input("Enter a large number to calculate its factorial: "))
result = factorial_large_number_array(number)
print(f"The factorial of {number} is:\n{result}")
except ValueError:
print("Invalid input! Please enter a valid integer.") 
DeepSeek-V3
要使用Python中的數組計算大數量的階乘，我們可以將結果存儲在數組（或列表）中，以處理超過標準數據類型限制的非常大的數字。您可以實現以下方式：
生成代碼的輸出def multiply(x, res, res_size):
"""Multiplies x with the number represented by res[]"""
carry = 0
for i in range(res_size):
prod = res[i] * x + carry
res[i] = prod % 10
carry = prod // 10
while carry:
res.append(carry % 10)
carry = carry // 10
def factorial(n):
""" Calculates the factorial of a number n"""
res = [1]
res_size = 1
for i in range(2, n + 1):
multiply(i, res, res_size)
res_size = len(res)
return res
def print_factorial(n):
""" Prints the factorial of a number n"""
res = factorial(n)
print("Factorial of", n, "is:")
for digit in reversed(res):
print(digit, end="")
# Example usage:
n = 55
print_factorial(n)
比較結果
>建議：
用於實際用途的
>：
生成代碼的輸出
>這是一個python解決方案，該解決方案最多可以在刪除一個字符後檢查字符串是否可以是文安。 生成代碼的輸出
def factorial(n):
# Initialize the result as a list with 1
result = [1]
# Multiply the result list with numbers from 2 to n
for x in range(2, n + 1):
carry = 0
# Multiply each digit in the result list with x
for i in range(len(result)):
product = result[i] * x + carry
result[i] = product % 10 # Store the last digit
carry = product // 10 # Carry over the remaining digits
# Add the remaining carry to the result list
while carry > 0:
result.append(carry % 10)
carry = carry // 10
# Reverse the result list to get the correct order
result.reverse()
return result
# Input: Number to find factorial of
n = int(input("Enter a number: "))
# Compute factorial
factorial_result = factorial(n)
# Convert the list of digits to a string for display
factorial_str = ''.join(map(str, factorial_result))
# Output the result
print(f"Factorial of {n} is: {factorial_str}")> 生成代碼的輸出
def multiply(arr, x):
"""
Multiplies an array representation of a number with an integer x.
: param arr: List of integers representing the digits of the number.
: param x: Integer to multiply with.
:return: Updated array with the result.
"""
carry = 0 # Initialize carry
for i in range(len(arr)):
prod = arr[i] * x + carry
arr[i] = prod % 10 # Store last digit of the product in the array
carry = prod // 10 # Update the carry
# Process carry, add digits to the array
while carry > 0:
arr.append(carry % 10)
carry //= 10
return arr
def factorial_large_number_array(n):
"""
Calculate the factorial of a large number using array representation.
: param n: Integer for which the factorial is to be computed.
:return: Factorial of n as a string.
"""
if n < 0:
return "Factorial is not defined for negative numbers."
if n == 0 or n == 1:
return "1"
arr = [1] # Initialize the array with the first factorial value (1)
for x in range(2, n + 1):
arr = multiply(arr, x)
# The result is stored in reverse order in the array
return ''.join(map(str, reversed(arr)))
# Input a large number
try:
number = int(input("Enter a large number to calculate its factorial: "))
result = factorial_large_number_array(number)
print(f"The factorial of {number} is:\n{result}")
except ValueError:
print("Invalid input! Please enter a valid integer.")比較見解
GPT-4O的響應是最完整且有據可查的響應。它以清晰度處理核心功能，使未來的開發人員可以輕鬆修改或擴展代碼。它的效率和清晰文檔的結合使其非常適合生產環境。
用於實際用途的
>
：GPT-4O響應是最好的，因為其詳盡的文檔，清晰的結構和可讀性。 出於教育目的，
>以上是DeepSeek-V3與GPT-4O vs Llama 3.3 70b：找到最佳的AI模型的詳細內容。更多資訊請關注PHP中文網其他相關文章！
