LiteLLM Bug: Model Provider Missing In Response

Hey everyone, let's dive into a curious bug report I've encountered while working with LiteLLM. It seems there's an issue where the model field in the response gets trimmed, specifically when using a model like 'groq/openai/gpt-oss-120b'. This little hiccup is causing some problems with cost calculations and model identification, especially when fallbacks are in play. Let's break down what's happening and how it impacts our workflows.

The Root of the Problem: Model Identification

So, here's the deal. I'm using LiteLLM to call a model, and I'm passing in the model parameter, along with other settings like messages, temperature, and max_tokens. Now, when I specify 'groq/openai/gpt-oss-120b' as the model, the response I get back doesn't include the full model identifier. Instead of getting 'groq/openai/gpt-oss-120b', I'm seeing 'openai/gpt-oss-120b'.

This might seem like a small detail, but it's causing a ripple effect. The primary issue arises when calculating the cost of the completion using the completion_cost(response) function. Because the response's model field doesn't match the actual model used (due to the missing provider information, like groq), the cost calculation goes awry. The function ends up looking under the wrong provider, leading to incorrect cost estimates. This is particularly problematic when you're using fallbacks – LiteLLM's clever feature that switches to another model if the primary one fails.

Imagine this: You're trying to use Groq's model, but it hits a snag. LiteLLM gracefully switches to an OpenAI model as a fallback. If the response doesn't accurately reflect that the OpenAI model was the one that actually provided the response, your cost tracking becomes a mess. You might end up paying for the wrong model or getting a skewed understanding of your model usage costs.

To be more specific, here's the Python code snippet I'm using:

response = await acompletion(
    model=model,
    messages=messages,
    stream=False,
    temperature=temperature,
    max_completion_tokens=max_tokens,
    fallbacks=fallbacks,
    num_retries=num_retries,
    timeout=timeout,
    response_format=response_format,
)

And the subsequent line that's failing:

cost = completion_cost(response)

As you can see, the model parameter is explicitly set, but the returned response is stripping the provider information, which breaks the cost calculation. This needs to be fixed since fallbacks are enabled, which is essential for knowing the actual model that handled the request and, therefore, how much it cost.

Impact and Implications

The impact of this bug is pretty significant, especially for those of us who rely on accurate cost tracking and model performance analysis. It affects:

• Cost Calculation: Inaccurate cost estimates lead to poor budgeting and resource allocation.
• Model Selection: The inability to correctly identify the model used makes it harder to evaluate the performance of different models and optimize your selection.
• Fallback Strategy: When fallbacks are active, you need to know which model was actually used to ensure you're making the most cost-effective choices.

Essentially, this bug undermines the reliability of the data we use to make informed decisions about our model usage. It makes it harder to understand how much we're spending, which models are performing best, and how to optimize our configurations for the best results.

Investigating the Issue

I'm on LiteLLM version v1.77.7, and this is where I'm seeing the behavior. As mentioned earlier, when the model parameter includes the provider, for example, 'groq/openai/gpt-oss-120b', the returned response strips the provider, leaving just 'openai/gpt-oss-120b'.

I've dug a bit deeper into the logs and haven't found any specific error messages that jump out at me, but the behavior is consistent. The problem seems to be within LiteLLM's handling of the model names, specifically when it parses the response. It's likely a parsing or formatting issue where the provider information is being unintentionally removed.

This is a tricky one because it seems like it's not a full-blown error, but a subtle one that has a cascade effect on the rest of the pipeline. Debugging it requires tracing the response object and figuring out where the provider information is being lost or modified.

Proposed Solutions and Workarounds

While we wait for a fix, here are a couple of potential workarounds:

1. Custom Parser: Create a custom parser for the model field in the response. This parser would check if the response's model starts with a recognized provider prefix (like groq/) and, if so, preserve the full model name. This approach requires modifying the code that handles the response, but it offers a direct solution.
2. Manual Cost Adjustment: If you can't modify the parser, you could manually adjust the costs based on a mapping of which models are used as fallbacks. This is less ideal, as it relies on manually tracking, which is prone to errors and hard to scale.
3. Model Aliases: Define aliases for models that include both the provider and the model name. This would mean using the full identifier when specifying the model, which could help maintain consistency. However, this doesn't fix the root cause.

These solutions are essentially temporary measures, but they can help mitigate the impact of the bug until it's fully addressed. I am hoping that the LiteLLM team can quickly address this, as correct model identification is vital to its usability, especially when fallbacks are in place.

Conclusion and Next Steps

This bug highlights the importance of accurate model identification in a system like LiteLLM, especially when cost tracking and fallback mechanisms are used. The loss of the provider information in the model field can lead to significant issues with cost calculation and model performance analysis.

I've submitted this bug report with the details, and version information. The next steps will be to see how the LiteLLM team plans to address this. In the meantime, I'll be implementing one of the proposed workarounds to ensure accurate cost tracking.

Hopefully, this detailed explanation gives everyone a good understanding of the issue and its implications. I will update this report with any progress or new information as I get it. Stay tuned for updates, and feel free to share any similar experiences or potential solutions you might have!

If you want to delve deeper into the technical aspects or want to propose a more sophisticated solution, reach out, and let's start a discussion!

For further information on cost optimization and model selection, check out this LiteLLM documentation site. They often have the latest updates and guides on how to get the most out of their framework.