---
license: mit
task_categories:
- question-answering
language:
- en
tags:
- instruction
pretty_name: cAI-PRISM-1600
size_categories:
- 1K<n<10K
---
# CompactAI-Prism-1600 

## High-Density Distillation Dataset for Small Model English Language Acquisition

**License:** MIT  
**Top-K:** 48  
**Source Model:** Qwen3.5 2B
**Primary Objective:** Teach small-scale AI models  to generate fluent, coherent English text through probability-aware distillation. Or at least help them sound less like they learned English from a fortune cookie.

---

## Overview

CompactAI-Prism is a specialized training dataset designed to accelerate English language acquisition in compact AI models. Unlike standard instruction-tuning datasets that provide only a (Prompt, Response) pair, CompactAI-Prism captures the decision landscape of the teacher model. For every token generated in the response, we record the Top-K alternative tokens and their associated log-probabilities.

Think of it as giving your tiny model a peek at the teacher's scratch paper during the exam. We won't tell if you don't.

This approach increases the information density of the dataset by Kx per prompt, allowing student models to learn not just what the model answered, but what else it considered and why those options were rejected.

### The Math (Yes, There Is Math)
If a standard dataset provides 1 signal per token, CompactAI-Prism provides K signals. We know, we know - you became an AI researcher to avoid math. Sorry.

Total Training Signals = (Tokens per Response) x (Number of Questions) x K

For this release:
- Tokens per response (x): {AVG_RESPONSE_TOKENS} (Too lazy to check this. Might update later)
- Number of questions (y): 1600
- Top-K value: 48
- Effective training examples: {AVG_RESPONSE_TOKENS} x 1600 x 48 = 76800 * {AVG_RESPONCE_TOKENS}

That is a lot of tokens. You are welcome.

---

## Why Call It PRISM?

Great question. We considered "CompactAI-Overthinker" and "TinyModelTherapySession", but those did not fit on a GitHub repo name.

The name "Prism" reflects the core mechanism of this dataset:

1. **Single Input, Spectrum Revealed**: Just as a prism takes a single beam of white light and refracts it to reveal the full spectrum of colors within, CompactAI-Prism takes a single AI response and refracts it to reveal the full spectrum of token probabilities that existed at each generation step. Also, prisms look cool in stock photos.

2. **Hidden Structure Made Visible**: A prism does not create new colors; it exposes what was already present but invisible to the naked eye. Similarly, this dataset does not alter the teacher model's output; it exposes the latent probability distribution that guided each token choice. Like an X-ray, but for indecision.

3. **Clarity Through Decomposition**: By decomposing the generation process into its constituent probabilistic components, we enable student models to learn with greater clarity. They see not only the path taken, but the roads not taken - and the relative likelihood of each. It is like watching a choose-your-own-adventure book write itself, then regretting every choice.

In short: Prism turns opaque generation into transparent learning. Or at least slightly less opaque. Baby steps.

---

## Series Purpose: English Language Foundation for Small Models

This dataset series is explicitly designed to teach small models to speak English. Because let us be honest - some of them really need it.

By exposing compact architectures to the full probability distribution of token choices made by a capable teacher model, we enable:

- Faster convergence on grammatical English structures (goodbye, "me want food")
- Improved token selection confidence in low-parameter regimes (no more second-guessing every comma)
- Better handling of ambiguous or open-ended prompts (sometimes a question is just a question)
- Reduced hallucination through uncertainty-aware training (your model will still lie, but with more confidence intervals)

---


## Data Format

The dataset is provided in JSONL format. Each line represents a complete conversation turn with embedded probability data.

### Schema

```json
{
  "messages": [
    {"role": "user", "content": "STRING"},
    {"role": "assistant", "content": "STRING"}
  ],
  "response_tokens": INT,
  "token_logprobs": [
    {
      "position": INT,
      "generated_token_id": INT,
      "generated_token": "STRING",
      "logprob": FLOAT,
      "top_k": [
        {"token_id": INT, "token": "STRING", "logprob": FLOAT}
      ]
    }
  ]
}
```




### Training Applications

1. **KL Divergence Distillation:** Use the full top_k distribution to minimize KL divergence between student and teacher, rather than just matching the chosen token. It is like teaching by example, but with more calculus.

2. **Confidence-Calibrated Generation:** Train small models to output confidence scores by learning from the teacher's logprob distributions. Now your tiny model can say "I am 73 percent sure that is correct" instead of just confidently being wrong.

3. **Alternative-Aware Decoding:** During inference, use knowledge of plausible alternatives to improve beam search or sampling strategies. Or just ignore this and use temperature=0.7 like everyone else. No judgment.

4. **English Fluency Bootstrapping:** Focus training on high-probability English token sequences to rapidly establish grammatical foundations in sub-10M parameter models. Because "me eat apple" is charming for about five minutes, then it gets old.

---

## Dataset Statistics

| Metric | Value |
|--------|-------|
| Total prompts | 1600 |
| Top-K per position | 48 |
| Number of times we questioned our life choices while building this | Yes |

---

## License

This project is licensed under the MIT License. Which is fancy legal speak for "use this however you want, just do not sue us when your tiny model starts writing poetry about toaster ovens."

Copyright (c) 2026 CompactAI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Translation: If your model learns to speak perfect English but also develops an existential crisis, that is on you.

---

## Source Attribution
- **Dataset:** Me.
- **Teacher Model:** Qwen3.5 2B
Thats it :)
---

## Citation

If you use CompactAI-Prism in your research or development, please cite:

```bibtex
@dataset{CompactAI-O/PRISM-K48-Gemma4.E2},
  title = {CompactAI-Prism: Top-48 Probability Distillation for Small Model English Training},
  author = {CompactAI},
  year = 2026,
  url = https://huggingface.co/datasets/CompactAI/cAI-Prism-K48,
}
```

Or just mention us in your paper's acknowledgments. We like hearing our names.

---


## Final Thoughts

Look, we are not perfect. This dataset might have bugs. The documentation might have typos. Our jokes might fall flat. But we tried. And in the grand tradition of small models everywhere, we believe that effort counts for something.

Now go train something tiny and wonderful. And if it starts speaking perfect English, maybe send us a note. We would love to hear about it.

Or not. No pressure. We have trust issues.