Update README.md

README.md

@@ -19,3 +19,115 @@ configs:
   - split: train
     path: data/train-*
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+
+# 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
+
+```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
+- 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** (this dataset)
+   - 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**
+   - 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_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](https://docs.vllm.ai/en/v0.8.3/serving/openai_compatible_server.html).
+
+## License
+
+This dataset is released under the MIT License.