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kevintran0310/Llama-3.2-3B-Darkweb-Classifier

sourceHugging Faceupdated 1y agoView on Hugging Face
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This model is a fine-tuned version of the meta-llama/Llama-3.1-8B-Instruct model, specialized in classifying textual content from dark web sources into a range of illicit and non-illicit content categories. The model was trained using QLoRA and PEFT for efficient low-resource finetuning.

Model Details

Model Description

This model was developed to identify and classify various dark web content categories, such as Cryptocurrency, DrugsIllegal, PornoChild-pornography, Hosting_Software, and many others. The training prompts follow a few-shot format that helps the LLM understand the classification context clearly.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

  • —Developed by: [Quoc Khoa Tran]
  • —Funded by [optional]: [Monash University]
  • —Shared by [optional]: [More Information Needed]
  • —Model type: [Causal Language Model (LLM for classification via instruction following)]
  • —Language(s) (NLP): [Multi-languages]
  • —License: [Apache 2.0 (inherited from base model)]
  • —Finetuned from model [optional]: [meta-llama/Llama-3.1-8B-Instruct]

Model Sources [optional]

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Uses

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Direct Use

This model is designed to be used as a text classification tool to label scraped or user-submitted content from dark web sources into pre-defined categories.

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Downstream Use [optional]

Integration into security monitoring systems

Filtering or tagging content for moderation

Academic research on illicit activity classification

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Out-of-Scope Use

Real-time moderation of critical systems without human oversight

Classifying general-purpose content outside of dark web contexts

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Bias, Risks, and Limitations

The model may incorrectly classify borderline or ambiguous content.

Imbalanced categories in training may bias predictions toward certain classes.

Misuse may lead to false positives in sensitive applications (e.g., legal enforcement).

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Recommendations

Always use human review in high-risk scenarios.

Evaluate the model in your own context before deployment.

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

The dataset consists of 4,178 samples of scraped text from dark web pages, each labeled with one of 40+ content categories (e.g., HostingFile-sharing, ViolenceWeapons, Social-Network_Blog).

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters
  • —Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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