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 ---
-base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
-library_name: peft
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
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-#### Hardware
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-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.15.2

 ---
+---
+license: mit
+datasets:
+- HuggingFaceH4/MATH
+language:
+- en
+tags:
+- math
+- number-theory
+- lora
+- quantized
+- tinyllama
+- reasoning
+- education
+inference:
+  parameters:
+    max_new_tokens: 256
+    temperature: 0.7
+    top_p: 0.95
+    top_k: 50
+---
+<div align="center">
+# lambdai · TinyLlama-1.1B finetuned on Number Theory
+[![Lambda Logo](https://raw.githubusercontent.com/lambdaindie/assets/main/lambda-banner.png)](https://huggingface.co/lambdaindie)
+</div>
+**lambdai** é o primeiro modelo oficial da organização **Lambda (Λ)** — uma startup solo angolana de pesquisa em IA liderada por Marius Jabami.
+Esse modelo foi finetunado a partir do [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) usando **LoRA + quantização em 8 bits**, com foco em **raciocínio matemático simbólico**, especialmente **teoria dos números**.
+---
+## Dataset
+Treinado com o subset `number_theory` do benchmark [HuggingFaceH4/MATH](https://huggingface.co/datasets/HuggingFaceH4/MATH), no split `test`, que contém problemas complexos de matemática com soluções detalhadas.
+---
+## Treinamento
+**Parâmetros LoRA**:
+- `r=8`, `alpha=16`
+- `target_modules=["q_proj", "v_proj"]`
+- `dropout=0.05`
+- Quantização 8-bit (QLoRA)
+**Formato de entrada:**
+```text
+Problem: <descrição do problema>
+Solution:
+---
+Exemplo de uso
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("lambdaindie/lambdai")
+tokenizer = AutoTokenizer.from_pretrained("lambdaindie/lambdai")
+prompt = "Problem: What is the smallest prime factor of 91?\nSolution:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+---
+Aplicações
+IA explicativa para matemática
+Tutores autônomos com raciocínio passo a passo
+Assistência em resolução simbólica
+Agentes educacionais
+Treinamento de reasoning agents
+---
+Sobre a Lambda
+Λ Lambda é uma startup indie fundada por Marius Jabami, com foco em IA educacional, modelos compactos e agentes autônomos. lambdai é parte do ΛCore, núcleo de pesquisa e experimentação em LLMs e raciocínio simbólico.
+---
+Links
+Lambda Indie @ Hugging Face
+TinyLlama Base Model
+Dataset: HuggingFaceH4/MATH
+---
+Licença
+MIT License — uso livre para fins educacionais, de pesquisa ou pessoais.
+---