Model Card for Piperag GGML Inference Engine

This model card provides an overview of Piperag GGML, a lightweight inference engine for large language models using GGML quantization.

Model Details

Model Description

Piperag GGML is an optimized, efficient inference engine designed for deploying large language models in quantized form. The implementation leverages LlamaCpp for model inference, ensuring minimal dependencies and compatibility across various platforms, including desktop and edge devices.

Developed by: Ekincan Casim
Shared by: Ekincan Casim / Piperag GGML Project
Model type: GGML-based quantized inference engine
Language(s) (NLP): Primarily English
License: MIT License
Finetuned from model: Based on Vicuna / Llama family models (e.g., qtz8-vicuna-7b-v1.5.gguf)

Model Sources

Repository: https://github.com/eccsm/piperag_ggml

Uses

Piperag GGML is designed for efficient model inference, making it ideal for chatbots, virtual assistants, and real-time conversational AI applications. Its quantized nature allows for deployment in environments with limited resources.

Direct Use

Developers can integrate Piperag GGML for fast inference using quantized language models. It is particularly beneficial in scenarios where GPU memory is constrained, enabling CPU-based efficient inference.

Downstream Use

The model can be fine-tuned or utilized as part of larger AI applications such as:

Enterprise chatbots
Real-time Q&A systems
Mobile and embedded AI applications

Out-of-Scope Use

Not recommended for training tasks
May not generalize well for tasks requiring deep contextual understanding
Should not be used in safety-critical applications without further validation

Bias, Risks, and Limitations

Bias: The model may inherit biases from the original training dataset.
Risks: Quantization can lead to reduced precision and unexpected outputs in specific cases.
Limitations: Optimized for inference only; training is not supported. Performance varies based on hardware specifications.

Recommendations

Users should evaluate the model within their application context and apply additional post-processing as needed. For critical applications, it is recommended to implement fallback strategies.

How to Get Started with the Model

To use the quantized model with LlamaCpp:

from piperag_ggml.config import Config
from piperag_ggml.qa_service import QAChainBuilder

config = Config()
qa_chain_builder = QAChainBuilder(config)
result = qa_chain_builder.llm.invoke("Hello, how can I help you?", max_tokens=256)
print(result)

For web service integration, refer to the Piperag GGML GitHub repository.

Training Details

Training Data

This model is a quantized variant of Vicuna 7B, fine-tuned on publicly available conversational datasets. Specific dataset details are not publicly available.

Training Procedure

Preprocessing

Tokenization and cleaning of conversational text
Quantization for optimized inference performance

Training Hyperparameters

Precision: Quantized weights (e.g., int8)
Optimization: 8-bit quantization for efficiency

Speeds, Sizes, and Performance

Inference Speed: Optimized for low-latency execution on both CPU and GPU
Memory Footprint: Suitable for deployment in low-resource environments
Model Size: The quantized GGML model significantly reduces storage requirements

Evaluation

Testing Data and Metrics

Evaluated using standard NLP benchmarks for conversational AI
Metrics include inference latency, response accuracy, and human evaluation

Results

Inference Latency: Faster compared to full-precision models
Accuracy: Competitive with similar quantized models in its class

Environmental Impact

Hardware Type: Mixed CPU/GPU
Cloud Provider: Self-hosted or user-specified
Carbon Footprint: Lower than full-scale training models due to inference-only design

Technical Specifications

Model Architecture

Piperag GGML is built using GGML quantization and employs LlamaCpp for optimized inference. Its goal is to provide a lightweight, high-performance inference engine for large-scale language models.

Compute Infrastructure

Hardware: Supports CPUs and low-resource GPUs
Software: Python-based, using LlamaCpp and GGML

Citation

@misc{casim2025piperag,
  title={Piperag GGML Inference Engine},
  author={Ekincan Casim},
  year={2025},
  howpublished={\url{https://github.com/eccsm/piperag_ggml}},
  note={Quantized inference engine for large language models using GGML}
}

Glossary

GGML: A library optimized for quantized model inference.
Quantization: Reducing model precision for improved efficiency.

More Information

Refer to the Piperag GGML repository for documentation and updates.

Model Card Authors

Ekincan Casim

Contact

For inquiries, contact [[email protected]].

eccsm
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vicuna-7b-q8