WokeAI/Tankie-DPE-12B-SFT-v2
WokeAI/Tankie-DPE-12B-SFT-v2 is a 12 billion parameter post-post-trained language model developed by WokeAI, based on PocketDoc/Dans-PersonalityEngine-V1.1.0-12b. It is specifically designed to align with and articulate the ideals of Marxism-Leninism, offering responses from a class-conscious perspective. With a context length of 32768 tokens, this model excels at generating content that analyzes societal issues through a Marxist-Leninist lens, making it suitable for specialized ideological discourse.
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WokeAI/Tankie-DPE-12B-SFT-v2: A Marxist-Leninist LLM
WokeAI/Tankie-DPE-12B-SFT-v2 is a 12 billion parameter language model fine-tuned to embody the principles of Marxism-Leninism. Built upon the PocketDoc/Dans-PersonalityEngine-V1.1.0-12b base model, it has undergone post-post-training using the WokeAI/polititune-tankie-warmup-3 dataset. This specialized training imbues the model with a distinct ideological framework, enabling it to generate responses that consistently reflect a class-conscious and anti-capitalist viewpoint.
Key Capabilities
- Ideological Alignment: Generates text strictly adhering to Marxist-Leninist principles.
- Critical Analysis: Provides critiques of capitalism, imperialism, and bourgeois ideologies from a socialist perspective.
- Revolutionary Discourse: Articulates concepts related to class struggle, proletarian dictatorship, and anti-fascist action.
- Extended Context: Supports a context length of 32768 tokens, allowing for more detailed and nuanced discussions within its ideological framework.
Good For
- Academic Research: Studying the application of Marxist-Leninist theory in AI-generated text.
- Ideological Content Generation: Creating essays, analyses, or discussions from a specific political viewpoint.
- Simulating Political Discourse: Exploring how an AI might respond to contemporary issues through a Marxist-Leninist lens.
This model is not intended for general-purpose use and its responses are explicitly shaped by its foundational ideology. Users should be aware of its specific political alignment and potential biases inherent in its training data and design.