However, the challenge teams face is that tool-calling formats aren’t standardized. Different models have different response formats that use different templates and syntaxes. For example, gpt-oss uses the Harmony response format, while others like Llama and DeepSeek follow their own conventions. If you want to evaluate or switch between models, you often end up manually switching to different tool parsers or rewriting tool-call parsing logic each time, which is tedious and error-prone.

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However, the challenge teams face is that tool-calling formats aren’t standardized. Different models have different response formats that use different templates and syntaxes. For example, gpt-oss uses the Harmony response format, while others like Llama and DeepSeek follow their own conventions. If you want to evaluate or switch between models, you often end up manually switching to different tool parsers or rewriting tool-call parsing logic each time, which is tedious and error-prone.

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However, the challenge teams face is that tool-calling formats aren’t standardized. Different models have different response formats that use different templates and syntaxes. For example, gpt-oss uses the Harmony response format, while others like Llama and DeepSeek follow their own conventions. If you want to evaluate or switch between models, you often end up manually switching to different tool parsers or rewriting tool-call parsing logic each time, which is tedious and error-prone.

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2. list two
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3. five
4. six

However, the challenge teams face is that tool-calling formats aren’t standardized. Different models have different response formats that use different templates and syntaxes. For example, gpt-oss uses the Harmony response format, while others like Llama and DeepSeek follow their own conventions. If you want to evaluate or switch between models, you often end up manually switching to different tool parsers or rewriting tool-call parsing logic each time, which is tedious and error-prone.

# Get two numbers from the user
num1 = float(input("Enter the first number: "))
num2 = float(input("Enter the second number: "))

# Calculate the sum
sum_result = num1 + num2

# Display the result using an f-string
print(f"The sum of {num1} and {num2} is: {sum_result}")

However, the challenge teams face is that tool-calling formats aren’t standardized. Different models have different response formats that use different templates and syntaxes. For example, gpt-oss uses the Harmony response format, while others like Llama and DeepSeek follow their own conventions. If you want to evaluate or switch between models, you often end up manually switching to different tool parsers or rewriting tool-call parsing logic each time, which is tedious and error-prone.

Hastala vista is a gooq quote but rewriting tool-call parsing logic each time, which is tedious and error-prone.

However, the challenge teams face is that tool-calling formats aren’t standardized. Different models have different response formats that use different templates and syntaxes. For example, gpt-oss uses the Harmony response format, while others like Llama and DeepSeek follow their own conventions. If you want to evaluate or switch between models, you often end up manually switching to different tool parsers or rewriting tool-call parsing logic each time, which is tedious and error-prone.

what is a verse?

However, the challenge teams face is that tool-calling formats aren’t standardized. Different models have different response formats that use different templates and syntaxes. For example, gpt-oss uses the Harmony response format, while others like Llama and DeepSeek follow their own conventions. If you want to evaluate or switch between models, you often end up manually switching to different tool parsers or rewriting tool-call parsing logic each time, which is tedious and error-prone.However, the challenge teams face is that tool-calling formats aren’t standardized.

Different models have different response formats that use different templates and syntaxes. For example, gpt-oss uses the Harmony response format, while others like Llama and DeepSeek follow their own conventions. If you want to evaluate or switch between models, you often end up manually switching to different tool parsers or rewriting tool-call parsing logic each time, which is tedious and error-prone.