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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
- lxcorp
---
# lambda-1v-1b — Lightweight Math & Logic Reasoning Model
**lambda-1v-1b** 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.
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## 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("lxcorp/lambda-1v-1b")
tokenizer = AutoTokenizer.from_pretrained("lxcorp/lambda-1v-1b")
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))
```
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About λχ Corp.
λχ Corp. is an indie tech corporation founded by Marius Jabami in Angola, focused on AI-driven educational tools, robotics, 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 lxcorp.ai and huggingface.co/lxcorp.
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Developed with care by Marius Jabami — Powered by ambition, faith, and open source.
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