jonasluehrs-jaai/synthetic_dataset_mid-mid

Synthetic Dataset: Medium Context, Medium Generation

Dataset Description

This is a synthetic benchmark dataset designed to test LLM inference performance in balanced workload scenarios. The dataset consists of 2,000 samples with randomly generated tokens that simulate realistic, mixed-workload patterns where both input and output lengths are moderate.

Use Cases

This dataset is ideal for benchmarking:

• Chat applications with conversational exchanges
• API services with typical request/response patterns
• General-purpose LLM deployments
• Balanced throughput and latency optimization

Dataset Characteristics

• Number of Samples: 2,000
• Prompt Length Distribution: Normal distribution
  • Mean: 1,000 tokens
  • Standard deviation: 300 tokens
• Response Length Distribution: Normal distribution
  • Mean: 1,000 tokens
  • Standard deviation: 300 tokens
• Tokenizer: meta-llama/Llama-3.1-8B-Instruct

Dataset Structure

Each sample contains:

• prompt: A sequence of randomly generated tokens (medium context)
• prompt_length: Number of tokens in the prompt
• response_length: Number of tokens in the response

{
    'prompt': str,
    'prompt_length': int,
    'response_length': int
}

Token Generation

• Tokens are randomly sampled from the vocabulary of the Llama-3.1-8B-Instruct tokenizer
• Each sample is independently generated with lengths drawn from the specified distributions
• The dataset ensures realistic token sequences while maintaining controlled length distributions

Related Datasets

This dataset is part of a suite of three synthetic benchmark datasets, each designed for different workload patterns:

1. synthetic_dataset_high-low

   • High context (32k tokens), low generation (200 tokens)
   • Focus: Prompt processing efficiency, TTFT optimization

2. 🔷 synthetic_dataset_mid-mid (this dataset)

   • Medium context (1k tokens), medium generation (1k tokens)
   • Focus: Balanced workload, realistic API scenarios

3. synthetic_dataset_low-mid

   • Low context (10-120 tokens), medium generation (1.5k tokens)
   • Focus: Generation throughput, creative writing scenarios

Benchmarking with vLLM

This dataset is designed for use with the vLLM inference framework. The vLLM engine supports a min_tokens parameter, allowing you to pass min_tokens=max_tokens=response_length for each prompt. This ensures that the response length follows the defined distribution.

Setup

First, install vLLM and start the server:

pip install vllm

# Start the vLLM server
vllm serve meta-llama/Llama-3.1-8B-Instruct

Usage Example

from datasets import load_dataset
from openai import OpenAI

# Load the dataset
dataset = load_dataset("jonasluehrs-jaai/synthetic_dataset_mid-mid")

# Initialize vLLM client (OpenAI-compatible API)
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="token-abc123",
)

# Use a sample from the dataset
sample = dataset['train'][0]

# Make a completion request with controlled response length
completion = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": sample['prompt']}],
    max_tokens=sample['response_length'],
    extra_body={"min_tokens": sample['response_length']},
)

print(f"Generated {len(completion.choices[0].message.content)} characters")

For more information, see the vLLM OpenAI-compatible server documentation.

License

This dataset is released under the MIT License.