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
- Lightweight: Optimized for speed and efficiency in constrained environments.
- General Purpose: Performs well on common NLP tasks like text classification, summarization, and conversational AI.
- 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:
1pip install transformers
2
3Loading the Model
4
5from transformers import AutoTokenizer, AutoModelForCausalLM
6
7# Load the tokenizer and model
8tokenizer = AutoTokenizer.from_pretrained("your-organization/spec-1-mini")
9model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-1-Mini")
10
11# Example usage
12input_text = "What is the purpose of Spec-1-Mini?"
13inputs = tokenizer(input_text, return_tensors="pt")
14outputs = model.generate(**inputs)
15print(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
-
Designed for English; performance on other languages is not guaranteed.
-
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},
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.
For questions or support, reach out at
support@svector.co.in
