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

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]