---
tags:
- text-generation-inference
- transformers
license: apache-2.0
language:
- en
datasets:
- openai/gsm8k
metrics:
- bleu
- perplexity
- rouge
pipeline_tag: text-generation
---

# 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](https://labs.robiai.com/)
- **Funded by:** [Robi](https://www.robiai.com/)
- **Model type:** Transformer-based language model  
- **Language(s):** English  
- **License:** Apache 2.0  

### Model Sources
- **Repository:** [Github](https://github.com/getrobi/lexa)
- **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)
```
or
 
# 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](https://robiai.com/contact)