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monadgeek/
fenra-V1

Name: fenra-V1
Availability: InStock

unsloth/Phi-3-medium-4k-instruct

adapter

mit

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Model Card for fenra-V1

Model Overview

fenra-V1 is a domain-specialized language model focused on procurement fraud detection, analysis, and related investigative tasks. It is currently under active development and fine-tuning using parameter-efficient techniques.

The model is designed to assist with identifying suspicious procurement patterns, generating investigative insights, and supporting analysts working in fraud detection and compliance domains.

⚠️ Note: Training is ongoing. The model is not yet merged and may produce unstable or inconsistent outputs.

Model Details

Model Description

fenra-V1 is a fine-tuned variant of Phi-3-medium-4k-instruct, adapted using QLoRA (Quantized Low-Rank Adaptation) for efficient training and deployment. The model leverages domain-specific data related to procurement fraud scenarios to enhance its contextual understanding and response quality in this niche.

Developed by: Fenra Project
Model type: Causal Language Model (Instruction-tuned)
Base model: unsloth/Phi-3-medium-4k-instruct
Fine-tuning method: QLoRA (parameter-efficient fine-tuning)
Training status: Ongoing (LoRA adapters not yet merged)
Language(s): English
License: MIT

Model Sources

Base Model: https://huggingface.co/unsloth/Phi-3-medium-4k-instruct
Training Dataset: https://huggingface.co/datasets/monadgeek/fenra/
Repository: To be added
Demo: To be added

Intended Uses

Direct Use

fenra-V1 can be used for:

Procurement fraud analysis
Risk flagging in procurement documents
Generating investigative summaries
Question answering within fraud/compliance contexts
Pattern recognition in suspicious transactions or tenders

Downstream Use

Integration into fraud detection platforms
Compliance and audit tooling
Risk scoring pipelines
Internal investigation assistants

Out-of-Scope Use

This model is not suitable for:

Legal decision-making without human oversight
Financial or regulatory compliance automation without validation
High-stakes decision systems
General-purpose reasoning outside its domain
Use cases requiring guaranteed factual accuracy

Bias, Risks, and Limitations

Limitations

Training is ongoing; outputs may be inconsistent
Domain bias toward procurement fraud scenarios
May hallucinate or fabricate details
Limited general-world knowledge beyond training scope
Not evaluated for fairness across demographic groups

Risks

Misinterpretation of model outputs as factual evidence
Over-reliance in investigative workflows
Potential false positives in fraud detection scenarios

Recommendations

Always use human-in-the-loop verification
Treat outputs as assistive, not authoritative
Validate findings with external data sources
Avoid use in automated enforcement or legal systems

Getting Started

Example usage (Transformers + PEFT):

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "unsloth/Phi-3-medium-4k-instruct"
lora_adapter = "your-fenra-lora-path"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)

model = PeftModel.from_pretrained(model, lora_adapter)

prompt = "Analyze this procurement record for fraud indicators: ..."
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))

monadgeek/fenra-V1