---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: transformers
license: mit
language:
  - en
  - pt
metrics:
  - accuracy
pipeline_tag: text-generation
tags:
  - hf-inference
  - education
  - logic
  - math
  - low-resource
  - transformers
  - open-source
  - causal-lm
  - lambdaindie
---

# lambdAI — Lightweight Math & Logic Reasoning Model

**lambdAI** is a compact, fine-tuned language model built on top of `TinyLlama-1.1B-Chat-v1.0`, designed for educational reasoning tasks in both Portuguese and English. It focuses on logic, number theory, and mathematics, delivering fast performance with minimal computational requirements.

## Model Architecture

- **Base Model**: TinyLlama-1.1B-Chat
- **Fine-Tuning Strategy**: LoRA (applied to `q_proj` and `v_proj`)
- **Quantization**: 8-bit (NF4 via `bnb_config`)
- **Dataset**: [`HuggingFaceH4/MATH`](https://huggingface.co/datasets/HuggingFaceH4/MATH) — subset: `number_theory`
- **Max Tokens per Sample**: 512
- **Batch Size**: 20 per device
- **Epochs**: 3

## Example Usage (Python)

```python

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("lambdaindie/lambdai")
tokenizer = AutoTokenizer.from_pretrained("lambdaindie/lambdai")

input_text = "Problema: Prove que 17 é um número primo."
inputs = tokenizer(input_text, return_tensors="pt")

output = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(output[0], skip_special_tokens=True))

```

About Lambda

Lambda is an indie tech startup founded by Marius Jabami in Angola, focused on AI-driven educational tools, automation, and lightweight software solutions. The lambdAI model is the first release in a planned series of educational LLMs optimized for reasoning, logic, and low-resource deployment.

Stay updated on the project at lambdaindie.github.io and huggingface.co/lambdaindie.


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Developed with care by Marius Jabami — Powered by ambition and open source.

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