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
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:
1from piperag_ggml.config import Config
2from piperag_ggml.qa_service import QAChainBuilder
3
4config = Config()
5qa_chain_builder = QAChainBuilder(config)
6result = qa_chain_builder.llm.invoke("Hello, how can I help you?", max_tokens=256)
7print(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
1@misc{casim2025piperag,
2 title={Piperag GGML Inference Engine},
3 author={Ekincan Casim},
4 year={2025},
5 howpublished={\url{https://github.com/eccsm/piperag_ggml}},
6 note={Quantized inference engine for large language models using GGML}
7}
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
Contact
For inquiries, contact [
ekincan@casim.net].
