---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---

# Spec-1-Mini 130M Parameters

Spec-1-Mini is a lightweight language model with 130 million parameters, designed for efficient natural language processing tasks. Its compact size makes it suitable for environments with limited computational resources while maintaining reliable performance on a variety of tasks.

## Model Details

- **Model Name**: Spec-1-Mini
- **Parameters**: 130M
- **Language**: English (`en`)
- **Purpose**: General-purpose natural language understanding and generation

## Key Features

1. **Lightweight**: Optimized for speed and efficiency in constrained environments.
2. **General Purpose**: Performs well on common NLP tasks like text classification, summarization, and conversational AI.
3. **Low Resource Requirements**: Runs on machines with limited hardware capabilities.

## Usage

### Installation

To use Spec-1-Mini, install the required dependencies and load the model into your application:

```bash
pip install transformers

Loading the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("your-organization/spec-1-mini")
model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-1-Mini")

# Example usage
input_text = "What is the purpose of Spec-1-Mini?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Model Card

For more details about this model and its configuration, see the model card.

Applications

Conversational AI

Text summarization

Sentiment analysis

Entity recognition

Translation (English-based)


Limitations

1. Designed for English; performance on other languages is not guaranteed.


2. Not suitable for highly complex tasks due to its limited size.



Ethical Considerations

Non-commercial Use: This model is distributed under the CC BY-NC 4.0 license. Use in commercial applications is prohibited without permission.

Bias and Fairness: As with any language model, outputs may reflect biases present in the training data. Users are encouraged to evaluate and monitor model outputs for unintended biases.


Citation

If you use Spec-1-Mini in your research or projects, please cite it as follows:

@misc{spec1mini2024,
  title={Spec-1-Mini: A Lightweight 130M Parameter Language Model},
  author={SVECTOR Research Lab},
  year={2020},
  url={https://github.com/svector-corporation}
}

Acknowledgments

Spec-1-Mini was developed by SVECTOR Research Lab. We thank the open-source community for their invaluable contributions to model training and deployment.


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For questions or support, reach out at support@svector.co.in