---
license: cc-by-4.0
datasets:
- ishanb3d/synthetic_qa
language:
- en
tags:
- question-answering
- llama
- tiny-model
- experimental
pipeline_tag: text-generation
---

# Tiny QA Model (2M)

A **2M-parameter** question-answering model built to probe the lower limits of how
small a usable generative QA model can be. It produces somewhat coherent responses
to questions, given its extreme size constraints.

## Model Details

- **Parameters:** ~2M (1.5M non-embedding)
- **Architecture:** Llama (loadable with any standard Llama-compatible loader)
- **Language:** English
- **Training data:** [ishanb3d/synthetic_qa](https://huggingface.co/datasets/ishanb3d/synthetic_qa)

## Prompt Format

Prompts should follow this exact format:

```
<bos>Question: What is the purpose of unit testing in software projects?\nAnswer:
```

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ishanb3d/atto-language-model"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "<bos>Question: What is the purpose of unit testing in software projects?\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Intended Use

This model is intended **exclusively for research and development** — for example,
studying small-model behavior, capability limits, and synthetic-data training dynamics.

## Limitations

At only 2M parameters, output quality is limited. Responses may be incoherent,
factually wrong, or otherwise unreliable, and the model should **not** be used in
production or any setting requiring accuracy or safety.

## License

Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).