---
license: apache-2.0
tags:
- reasoning
- multilingual
- transformer
- robi-labs
- delta
- lexa
- lexa-family
- lexa-delta
pipeline_tag: text-generation
---

# Model Card for Lexa-Delta

Lexa-Delta is a multilingual reasoning large language model, developed by **Robi Labs**, designed for structured reasoning and natural conversation across multiple languages. It has been trained independently by Robi Labs.
---

## Model Details

### Model Description

* **Developed by:** Robi Labs
* **Model type:** Causal language model (decoder-only transformer)
* **Language(s):** Multilingual (English, Spanish, French, German, Chinese, Hindi, and more)
* **License:** Custom License (see LICENSE file)

### Model Sources

* **Repository:** [https://huggingface.co/RobiLabs/Lexa-Delta](https://huggingface.co/RobiLabs/Lexa-Delta)
* **Website:** [https://labs.robiai.com](https://labs.robiai.com)
* **Lexa Chat:** [https://lexa.chat](https://lexa.chat) (coming soon)
* **Socials:**

  * [Twitter/X](https://twitter.com/justlexait)
  * [LinkedIn](https://www.linkedin.com/company/robilabsai)
  * [Instagram](https://www.instagram.com/robilabs)

---

## Uses

### Direct Use

Lexa-Delta can be used directly for:

* Multilingual question answering
* Chain-of-thought reasoning
* Conversational AI assistants
* Educational support (explaining concepts across languages)

### Downstream Use

* Fine-tuning for domain-specific tasks (e.g., legal, medical, educational)
* Integration into applications and chat platforms

### Out-of-Scope Use

* Disallowed or harmful content generation
* High-stakes decision making without expert human oversight

---

## Bias, Risks, and Limitations

* May inherit biases from multilingual training data
* Reasoning ability may vary by language
* Can generate incorrect or hallucinated outputs

### Recommendations

Users should:

* Verify important information independently
* Avoid high-stakes reliance without human review
* Use responsibly with awareness of multilingual limitations

---

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("RobiLabs/Lexa-Delta", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("RobiLabs/Lexa-Delta")

messages = [
    {"role": "system", "content": "You are Lexa-Delta, a multilingual reasoning model from Robi Labs."},
    {"role": "user", "content": "What is the capital of Armenia?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## Training Details

### Training Data

* Multilingual reasoning data (sources undisclosed)

### Training Procedure

* **Method:** Full training 
* **Precision:** Mixed precision 
* **Compute:** B200 GPUs

#### Training Hyperparameters

* **Learning rate:** 2e-4
* **Sequence length:** 4096 tokens
* **Gradient accumulation:** enabled

---

## Environmental Impact

* **Hardware Type:** B200 GPUs
* **Region:** Not disclosed
* **Carbon Emitted:** Not disclosed

---

## Technical Specifications

### Model Architecture and Objective

* Decoder-only transformer
* Objective: Causal language modeling with reasoning-oriented training

### Compute Infrastructure

* **Hardware:** B200 GPUs
* **Software:** PyTorch, Hugging Face Transformers

---

## Citation

```bibtex
@misc{lexa-delta,
  title={Lexa-Delta: A Multilingual Reasoning LLM},
  author={Robi Labs},
  year={2025},
  howpublished={\url{https://huggingface.co/RobiLabs/Lexa-Delta}},
}
```

---

## Model Card Authors

[Robi Labs](https://labs.robiai.com)

## Model Card Contact

* Website: [labs.robiai.com](https://labs.robiai.com)
* Email: [labs@robiai.com](mailto:labs@robiai.com)