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---
task_categories:
- question-answering
- text-generation
language:
- en
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: 1k
path: "dynamic_sonnet_llama_2_prefix_256_max_1024_1024_sampled.parquet"
- split: 2k
path: "dynamic_sonnet_llama_2_prefix_512_max_2048_1024_sampled.parquet"
- split: 4k
path: "dynamic_sonnet_llama_2_prefix_1024_max_4096_1024_sampled.parquet"
- split: 8k
path: "dynamic_sonnet_llama_2_prefix_2048_max_8192_1024_sampled.parquet"
---
# Dynamic Sonnet - Llama2
*Curated dataset for benchmarking LLM serving systems*
In real-world service scenarios, each request comes with varying input token lengths.
Some requests generate only a few tokens, while others produce a significant number.
Traditional fixed-length benchmarks fail to capture this variability, making it difficult to accurately assess real-world throughput performance.
This dynamic nature of input token lengths is crucial as it directly affects key features of LLM serving systems, such as continuous batching, which are essential for optimal performance.
To address this challenge, we introduce ***Dynamic Sonnet***—a dataset designed specifically for benchmarking LLM serving systems under realistic conditions.
***Dynamic Sonnet*** comprises four subsets: 1k, 2k, 4k, and 8k. Each subset is carefully curated to have an average token length of 512, 1k, 3k, and 7k, respectively.
This variability in token length within the dataset allows for a more accurate and comprehensive evaluation of LLM serving systems in environments that mirror real-world usage.
Furthermore, in real-world scenarios, requests often share common prefixes. Advanced systems can leverage this by caching these prefixes to boost performance.
***Dynamic Sonnet*** simulates this behavior by incorporating a common prefix that constitutes approximately 25% of the maximum length in each subset (N/4 for an Nk subset).
This design allows for more realistic benchmarking of systems that optimize for such efficiencies.
## Details
The Dynamic Sonnet dataset consists of five columns: `id`, `system_prompt`, `user_prompt`, `formatted_input` and `tok_inputs`
* `id`: A unique identifier (index) for each prompt
* `system_prompt`: A common prefix that instructs the agent to select specific lines from the following text
* `user_prompt`: The lines selected from Shakespeare's sonnets
* `formatted_input`: The prompt(`system_prompt`+`user_prompt`) formatted according to a specific chat template
* `tok_inputs`: The tokenized version of the `formatted_input`
## Usage
To benchmark with ***Dynamic Sonnet***, users can pass the token IDs (tok_inputs) directly to the LLM serving system.
For benchmarking an OpenAI-compatible system, users can concatenate the `system_prompt` and `user_prompt`, and then send a request to `v1/chat/completions` endpoint, using the concatenated result as the request body.
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