This is the 4-bit NF4 quantized LLM component of RADDICL 2.0 (Retrieval Augmented Deception Detection through In-Context Learning), a domain-agnostic deception detection system.

Base model: Intel/neural-chat-7b-v3-3 (Mistral 7B architecture)
Quantization: 4-bit NF4 via BitsAndBytes, double quantization enabled
Compute dtype: float16
Total parameters: 3.75B (~3.74 GB estimated memory footprint)

For the full RAG pipeline and demo, see cdenq/raddicl2-demo.

Model Details

Property	Value
Architecture	`MistralForCausalLM`
Base model	`Intel/neural-chat-7b-v3-3`
Quantization method	BitsAndBytes `nf4`, double quant
Compute dtype	`float16`
Max position embeddings	32768
Sliding window	4096
Vocab size	32000
Attention	SDPA

How to Load

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import snapshot_download

# Download model files
model_path = snapshot_download(repo_id="cdenq/raddicl2-demo-model")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)

# Load quantized model (quantization config is embedded in config.json)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    trust_remote_code=True,
)

The quantization_config is already embedded in config.json, so no extra BitsAndBytesConfig is needed when loading.

Intended Use

This model is the generation component of the RADDICL 2.0 deception detection pipeline. Given a structured few-shot prompt (constructed by the RADDICL 2.0 RAG pipeline), it produces a classification label (deceptive / non-deceptive) and step-by-step reasoning.

It is not intended to be used as a standalone general-purpose chat model.

Citation

(Citation for RADDICL 2.0 will be updated upon publication.)

Acknowledgments

Developed by Christopher Denq and Dr. Rakesh Verma at the ReDAS Lab, University of Houston.

redasers/raddicl2-demo-model

RADDICL 2.0 — Quantized LLM

Model Details

How to Load

Intended Use

Citation

Acknowledgments

redasers/
raddicl2-demo-model