Model Card for Lexa-T1 (Lexa Think)

Model Details

Model Description

Lexa-T1 (Lexa Think) is optimized for enhanced reasoning and text generation. It is designed to assist in various NLP applications, including content creation, knowledge retrieval, and conversational AI.

Developed by: Robi Labs
Funded by: Robi
Model type: Transformer-based language model
Language(s): English
License: Apache 2.0

Model Sources

Repository: Github
Demo: [Will Be Available Soon]

Uses

Direct Use

Lexa-T1 can be used directly for text generation tasks such as:

AI-powered assistants
Automated content creation
Summarization and paraphrasing
Question-answering and knowledge retrieval

Downstream Use

Lexa-T1 can be further fine-tuned for domain-specific applications, such as:

Legal document analysis
Technical documentation generation
Marketing and creative writing assistance

Out-of-Scope Use

The model is not intended for:

Generating misinformation
Producing biased or harmful content
High-stakes decision-making without human supervision

Bias, Risks, and Limitations

While Lexa-T1 has been fine-tuned to improve accuracy and reliability, it still inherits biases from its training data. Users should exercise caution when using the model for critical applications.

Recommendations

Regularly review generated content for factual accuracy.
Avoid using the model for sensitive or high-risk applications without human oversight.
Ensure compliance with ethical AI principles and guidelines.

How to Get Started with the Model

Use the following code to load and use Lexa-T1:

Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="robiai/lexa-t1")
pipe(messages)

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("robiai/lexa-t1")
model = AutoModelForCausalLM.from_pretrained("robiai/lexa-t1")

Training Details

Training Data

The model has been fine-tuned using diverse datasets to improve generalization across various NLP tasks. Further details on the dataset will be provided in upcoming updates.

Training Procedure

Preprocessing

Tokenization performed using AutoTokenizer
Text cleaning and normalization applied

Training Hyperparameters

Precision: Mixed-precision (fp16)
Batch size: 1
Optimizer: AdamW
Learning rate: [5e-6]

Evaluation

Testing Data, Factors & Metrics

Testing Data

Lexa-T1 has been evaluated on standard NLP benchmarks to measure its performance.

Metrics

Perplexity 63.08
BLEU score 18.58
ROUGE score 0.56

Results

The model achieves competitive performance on text-generation benchmarks, with further evaluation ongoing.

Environmental Impact

Hardware Type: T4-GPU
Cloud Provider: Google Colab

Technical Specifications

Model Architecture and Objective

Lexa-T1 follows the transformer-based architecture optimized for causal language modeling.

Compute Infrastructure

Hardware

Trained on GPUs with high-memory capacity

Software

Hugging Face Transformers
PyTorch
Unsloth library for efficient fine-tuning

Citation

If using Lexa-T1 in research or production, please cite:

Robi Labs (Robi Team). (2025). Lexa-T1: An Advanced AI Model for Text Generation and Summarization. *Robi Team*.

Contact

For inquiries or support, contact the Robi Team at Contact Page

robiai
/

lexa-t1