language: en
license: other
tags:
- quantized
- nlp
- transformers
- text-generation
pipeline_tag: text-generation
Model Card for Quantized Llama3-4x8B-Time-Agents
Model Description
This model is a quantized version of the Llama3-4x8B-Time-Agents, optimized for efficient inference. The model leverages 4-bit precision quantization, substantially enhancing computational efficiency by reducing memory demands and accelerating response times, making it particularly well-suited for deployment in resource-constrained environments.
Technical Specifications
- Model Type: Transformer-based Quantized Model
- Quantization Level: 4-bit (Q4_K_M)
- Architecture: Retains the transformer architecture optimized for time-sensitive and multi-agent systems.
- Quantization Tool Used: llamacpp (a custom tool based on llama.cpp for model quantization)
- Performance Impact: Reduced precision with a moderate impact on accuracy and a significant increase in inference speed.
Usage
This quantized model is ideal for applications requiring fast response times, such as mobile or embedded devices and on-premises servers without dedicated GPU capabilities.
Quick Start
Code for inference will be published soon.
Limitations and Ethical Considerations
- Bias and Fairness: This model, like any AI, may carry biases from its training data. It is crucial to evaluate its performance in specific contexts.
- Quantization Effects: The reduced precision might affect nuanced understanding and generation, particularly in complex scenarios.
Environmental Impact
The model's quantization reduces computational demands, lowering the environmental impact associated with its operation, specifically in terms of energy consumption and carbon footprint.
Model and Tokenizer Download
You can download the quantized model and its tokenizer directly from the Hugging Face Hub:
- Full Model(unq): Download link
- Tokenizer: Included in the above link
Evaluation Results
Still under evaluation
Conclusion
This model card introduces the quantized version of the Llama3-4x8B-Time-Agents, providing necessary details to help potential users understand its capabilities and integrate it into their solutions effectively.