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
Prompt Format
Prompts should follow this exact format:
<bos>Question: What is the purpose of unit testing in software projects?\nAnswer:
Usage
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.
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