Datasets:

jonasluehrs-jaai
/

synthetic_dataset_high-low

dataset_info:
  features:
    - name: messages
      dtype: string
    - name: prompt_length
      dtype: int64
    - name: response_length
      dtype: int64
  splits:
    - name: train
      num_bytes: 423667776
      num_examples: 2000
  download_size: 384009093
  dataset_size: 423667776
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Synthetic Dataset: High Context, Low Generation

Dataset Description

This is a synthetic benchmark dataset designed to test LLM inference performance in high-context, low-generation scenarios. The dataset consists of 2,000 samples with randomly generated tokens that simulate real-world workloads where models process long input contexts but generate relatively short responses.

Use Cases

This dataset is ideal for benchmarking:

Document analysis with short answers
Long-context Q&A systems
Information extraction from large documents
Prompt processing efficiency and TTFT (Time-to-First-Token) optimization

Dataset Characteristics

Number of Samples: 2,000
Prompt Length Distribution: Normal distribution
- Mean: 32,000 tokens
- Standard deviation: 10,000 tokens
Response Length Distribution: Normal distribution
- Mean: 200 tokens
- Standard deviation: 50 tokens
Tokenizer: meta-llama/Llama-3.1-8B-Instruct

Dataset Structure

Each sample contains:

prompt: A sequence of randomly generated tokens (high 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:

🔷 synthetic_dataset_high-low (this dataset)
- High context (32k tokens), low generation (200 tokens)
- Focus: Prompt processing efficiency, TTFT optimization
synthetic_dataset_mid-mid
- Medium context (1k tokens), medium generation (1k tokens)
- Focus: Balanced workload, realistic API scenarios
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_high-low")

# 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.