Piperag GGUF Inference Engine — Vicuna 7B v1.5 Q4_1

This model card provides an overview of Piperag GGUF, a lightweight inference engine for large language models using GGUF quantization, featuring Vicuna 7B v1.5 quantized to Q4_1.

Model Details

Model Description

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

Developed by: Ekincan Casim
Shared by: Ekincan Casim / Piperag GGUF Project
Model type: GGUF-based quantized inference engine
Language(s) (NLP): Primarily English
License: MIT License
Finetuned from model: Based on Vicuna / LLaMA family models (e.g., vicuna-7b-v1.5-gguf-q4_1.gguf)

Model Sources

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

Uses

Direct Use

Piperag GGUF is designed for efficient model inference, making it ideal for chatbots, virtual assistants, and real-time conversational AI applications. Its Q4_1 quantized nature allows for deployment in environments with limited resources.

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 (Q4_1) 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 Llama.cpp:

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 GGUF GitHub repository.

Training Details

Training Data

This model is a quantized variant of Vicuna 7B v1.5, fine-tuned on publicly available conversational datasets. Specific dataset details are not publicly disclosed.

Training Procedure

Preprocessing

Tokenization and cleaning of conversational text
Quantization (Q4_1) for optimized inference performance

Training Hyperparameters

Precision: Quantized weights (Q4_1, 4-bit precision with optimized accuracy)
Optimization: 4-bit quantization (Q4_1) for balance between performance and 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: Significantly reduced storage requirements due to Q4_1 quantization

Evaluation

Testing Data and Metrics

Evaluated using standard NLP benchmarks for conversational AI.
Metrics include:

Inference latency
Response accuracy
Human evaluation

Results

Inference Latency: Faster compared to full-precision models
Accuracy: Competitive with similar quantized models (Q4_1 offers better accuracy than Q4_0)

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 GGUF is built using GGUF quantization and employs Llama.cpp for optimized inference. It aims 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 Llama.cpp and GGUF

Citation

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

Glossary

GGUF: A library and format optimized for quantized model inference.
Quantization: Reducing model precision (e.g., Q4_1) for improved efficiency.
Llama.cpp: A C++ implementation for efficient LLaMA and Vicuna inference.

More Information

Refer to the Piperag GGUF GitHub repository for documentation and updates.

Model Card Authors

Ekincan Casim

Contact

For inquiries, contact: [email protected]

eccsm
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vicuna-7b-v1.5-gguf-q4_1