README.md

---
dataset_info:
  features:
  - name: messages
    dtype: string
  - name: prompt_length
    dtype: int64
  - name: response_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 528492
    num_examples: 2000
  download_size: 467665
  dataset_size: 528492
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
task_categories:
- text-generation
size_categories:
- 1K<n<10K
tags:
- synthetic
- benchmarking
- vllm
- llm-inference
---

# Synthetic Dataset: Low Context, Medium Generation

## Dataset Description

This is a synthetic benchmark dataset designed to test LLM inference performance in **low-context, mid-generation** scenarios. The dataset consists of 2,000 samples with randomly generated tokens that simulate workloads where models receive short prompts but generate longer responses.

### Use Cases

This dataset is ideal for benchmarking:
- **Creative writing** and content generation
- **Code generation** from brief descriptions
- **Story/article generation** from short prompts
- **Generation throughput** and token-per-second optimization

### Dataset Characteristics

- **Number of Samples**: 2,000
- **Prompt Length Distribution**: Beta distribution (skewed toward lower values)
  - Alpha (α): 2.0 (controls left tail)
  - Beta (β): 6.0 (controls right tail)
  - Range: 10-120 tokens
- **Response Length Distribution**: Normal distribution
  - Mean: 1,500 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 (low context)
- `prompt_length`: Number of tokens in the prompt
- `response_length`: Number of tokens in the response

```python
{
    '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
- Prompt lengths follow a beta distribution, creating a realistic skew toward shorter prompts
- Response lengths follow a normal distribution, simulating typical generation patterns
- Each sample is independently generated with lengths drawn from the specified 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**
   - Medium context (1k tokens), medium generation (1k tokens)
   - Focus: Balanced workload, realistic API scenarios

3. **🔷 synthetic_dataset_low-mid** (this dataset)
   - 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:

```bash
pip install vllm

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

### Usage Example

```python
from datasets import load_dataset
from openai import OpenAI

# Load the dataset
dataset = load_dataset("jonasluehrs-jaai/synthetic_dataset_low-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](https://docs.vllm.ai/en/v0.8.3/serving/openai_compatible_server.html).

## License

This dataset is released under the MIT License.