Mistral-7B-Instruct-v0.3-Forensics-v1 Open-source Model - Supports Forensic Investigation Reasoning and Knowledge Retrieval

Mistral 7B Instruct V0.3 Forensics V1

Developed by gerasmark

This model is a fine-tuned version optimized from Mistral-7B-Instruct-v0.3, specifically designed for Q&A tasks in the field of forensic investigations, supporting advanced forensic reasoning and rapid knowledge retrieval.

Large Language Model

Transformers

#Forensic Knowledge Q&A #LoRA Fine-tuning #High-precision Forensic Reasoning

Downloads 28

Release Time : 4/25/2025

Model Overview

The model was trained using a specialized dataset named Advanced_Forensic_Investigations_Knowledge_Library_v1, containing approximately 100 domain-specific Q&A pairs, aiming to provide high-precision assistance in the forensic field.

Model Features

Forensic Domain Optimization

Designed specifically for forensic investigations, providing high-precision Q&A support.

LoRA Fine-tuning

Fine-tuned using the LoRA (Low-Rank Adaptation) method, adapting to specific tasks while maintaining the base model's performance.

Efficient Training

Training duration was only about 30 minutes, utilizing 4-bit quantization technology to reduce computational resource requirements.

Model Capabilities

Forensic Knowledge Q&A

Advanced Forensic Investigation Reasoning

Rapid Knowledge Retrieval

Use Cases

Forensic Investigation

Forensic Evidence Analysis

Assists in analyzing forensic evidence, providing relevant knowledge and reasoning support.

Fully aligned with reference answers within the evaluation dataset (BLEU-4, ROUGE-1, ROUGE-2 all at 100%).

Forensic Knowledge Retrieval

Quickly retrieves professional knowledge in the forensic field to improve investigation efficiency.

🚀 Forensic Mistral-7B-Instruct-v0.3 Fine-tuned Model

This model is a fine-tuned version of Mistral-7B-Instruct-v0.3, optimized for answering questions in forensic investigations, aiming to support advanced reasoning, rapid knowledge retrieval, and high-precision assistance in the forensic domain.

🚀 Quick Start

This README provides detailed information about a fine - tuned model based on Mistral-7B-Instruct-v0.3 for forensic investigations.

✨ Features

Optimized for answering forensic investigation questions.
Trained on a specialized dataset for high - precision domain assistance.
Achieved perfect scores on evaluation metrics within the evaluation dataset.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Summary

This model is a fine - tuned version of the Mistral-7B-Instruct-v0.3 optimised for answering questions in the domain of forensic investigations. The model has been trained using a specialised dataset titled Advanced_Forensic_Investigations_Knowledge_Library_v1, which consists of approximately 100 domain - specific question - answer pairs. The objective is to support advanced forensic investigative reasoning, rapid knowledge retrieval, and high - precision forensic domain assistance.

Model Details

Model Description

Property	Details
Model Type	Instruction - following Language Model (LoRA - based fine - tuning)
Language(s)	English
Fine - tuned from model	Mistral-7B-Instruct-v0.3

Training Details

Training Data

Property	Details
Dataset	`Advanced_Forensic_Investigations_Knowledge_Library_v1`
Data size	~100 high - quality, domain - specific QA pairs

Training Procedure

Preprocessing

Template: mistral
Token truncation/cutoff: 2048
No vocab resizing or prompt packing

Hyperparameters

Finetuning type: LoRA
Precision: bf16
LoRA rank: 16
LoRA alpha: 32
Batch size: 4
Gradient accumulation: 8
Learning rate: 3e - 4
Epochs: 35
LR scheduler: cosine
Quantisation: 4 - bit (bitsandbytes)
Cutoff length: 2048

Compute

Training time: close to 30 minutes
Framework: LLaMA - Factory

Evaluation

Testing Data, Factors & Metrics

Metrics Used: BLEU - 4, ROUGE - 1, ROUGE - 2
Results:
- BLEU - 4: 100%
- ROUGE - 1: 100%
- ROUGE - 2: 100%

These scores reflect perfect overlap with reference answers within the scope of the evaluation dataset.

Technical Specifications

Model Architecture and Objective

Base: Transformer (Mistral - 7B architecture)
Fine - tuning method: LoRA
Objective: Instruction - following with forensic legal knowledge adaptation

Compute Infrastructure

Hardware: 2xL40s
Software: LLaMA - Factory, PyTorch, Transformers, bitsandbytes

🔧 Technical Details

The model is based on the Mistral - 7B architecture, using LoRA for fine - tuning. It is trained on a specialized forensic investigation dataset to adapt to the forensic legal knowledge domain. The preprocessing, hyperparameters, and training procedure are carefully designed to optimize the model's performance in answering forensic - related questions. The evaluation results show that the model has excellent performance within the scope of the evaluation dataset.

📄 License

No license information is provided in the original document, so this section is skipped.

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