Project Void

Repository: github.com/SaBA26-void/project_void · Author: @SaBA26-void

Entropy-aware prompt compression for large language models.

Project Void analyzes token probability and entropy to remove low-information tokens while preserving semantic structure.

Designed for:

• Prompt optimization
• Token-cost reduction
• LLM preprocessing pipelines
• Entropy-based NLP experiments

Installation

Requires Node.js 18+.

npm install project_void

On first use, the Hugging Face model (e.g. Xenova/gpt2) is downloaded from the network and cached locally.

Install size

The published project_void package is small (source only). npm install also pulls in @huggingface/transformers (ONNX Runtime, native addons), which is typically hundreds of MB on disk. That footprint comes from running a local causal LM, not from this library’s compression code.

Usage

Probability mode

import { compressPrompt } from "project_void";

const result = await compressPrompt(
  "Xenova/gpt2",
  // optional
  { dtype: "fp32" }, // "q4"|"q5"|"q8"|"fp16"|"fp32"   by default {dtype = "fp16"}
  "What is the capital city of France?",
  {
    probability: 0.1,
  },
);

console.log(result.text);

Entropy mode

import { compressPrompt } from "project_void";

const result = await compressPrompt("Xenova/gpt2", {}, prompt, { entropy: 7 });

// or with explicit dtype:

const result = await compressPrompt("Xenova/gpt2", { dtype: "q8" }, prompt, {
  entropy: 7,
});

console.log(result.text);

Full Example

import { compressPrompt } from "project_void";

const prompt = "What is the capital city of France?";

const result = await compressPrompt("Xenova/gpt2", {}, prompt, {
  probability: 0.1,
});

const originalTokenCount =
  result.kept.reduce((s, w) => s + w.tokenCount, 0) +
  result.removed.reduce((s, w) => s + w.tokenCount, 0);

const compressedTokenCount = result.kept.reduce((s, w) => s + w.tokenCount, 0);

console.log(`Original: "${prompt}" (${originalTokenCount} tokens)`);

console.log(
  `Compressed: "${result.text.trim()}" (${compressedTokenCount} tokens)`,
);

console.log("\nKept:");

for (const w of result.kept) {
  console.log(
    `"${w.text}" prob=${w.probability.toFixed(4)} entropy=${w.entropy.toFixed(2)} bits`,
  );
}

console.log("\nRemoved:");

for (const w of result.removed) {
  console.log(
    `"${w.text}" prob=${w.probability.toFixed(4)} entropy=${w.entropy.toFixed(2)} bits`,
  );
}

API

compressPrompt(modelId, quantization, prompt, options)

Compresses text using token-level probability or entropy filtering.

Parameters

| Name | Type | Description | | --------------------- | -------- | -------------------------------------------------- | | modelId | string | Hugging Face model identifier (e.g. Xenova/gpt2) | | quantization | object | quantization alocas q4 4 bits fp16 16bits etc | | prompt | string | Input text | | options.probability | number | Keep tokens below probability threshold | | options.entropy | number | Keep tokens above entropy threshold |

Use either:

• probability

or

• entropy

Returns

{
  text: string,
  kept: Word[],
  removed: Word[]
}

Each word contains:

{
  text: string,
  probability: number,
  entropy: number,
  tokenCount: number
}

Mutual exclusivity: you must pass probability or entropy, not both; omitting both throws. First word always kept: compressAndJoin uses alwaysKeepFirst = true, so the first word is never removed regardless of threshold. Word shape: returned words also include id and ids (internal fields from merging), not just text, probability, entropy, tokenCount.

Compression Strategy

Probability mode

Keeps low-probability (informative) tokens.

Entropy mode

Keeps high-entropy (uncertain / information-rich) tokens.

Example Output

{
  text: "capital France",
  kept: [...],
  removed: [...]
}

Features

• Token probability pruning
• Entropy-based filtering
• Claude Shannon information theory
• Hugging Face transformer support (@huggingface/transformers)
• ESM package with a single public export: compressPrompt
• Token-level analysis output
• Research-friendly compression pipeline

Use Cases

• Reduce LLM prompt cost
• Compress retrieval context
• Token saliency analysis
• Prompt engineering experiments
• Information-density filtering

License

MIT

Author

Saba Bakuradze — @SaBA26-void

• Project repository: SaBA26-void/project_void
• GitHub profile: github.com/SaBA26-void