darkc0de/3.2test

TEXT GENERATIONConcurrency Cost:2Model Size:24BQuant:FP8Ctx Length:32kArchitecture:Transformer Cold

The darkc0de/3.2test model is a 24 billion parameter instruction-tuned language model, derived from Mistral-Small-3.2-24B-Instruct-2506. Developed by darkc0de, this model has been 'decensored' using the Heretic v1.1.0 tool, significantly reducing refusal rates compared to its base model. It features a standard Mistral architecture without a vision encoder, making it suitable for text-only applications requiring less restrictive content generation.

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darkc0de/3.2test: Decensored Mistral-Small-3.2-24B-Instruct-2506

This model, developed by darkc0de, is a 24 billion parameter instruction-tuned language model based on mistralai/Mistral-Small-3.2-24B-Instruct-2506. It has been specifically modified using the Heretic v1.1.0 tool to create a 'decensored' version.

Key Characteristics & Modifications

  • Base Model: Derived from anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only.
  • Decensoring: Utilizes 'abliteration' parameters, including specific weight adjustments for attn.o_proj and mlp.down_proj layers, to reduce content restrictions.
  • Refusal Rate: Demonstrates a significantly lower refusal rate (4/100) compared to the original model (97/100), as measured by KL divergence (0.1261).
  • Architecture: Maintains the standard Mistral architecture.
  • Capabilities: Text-only model; it does not include a vision encoder.

Primary Differentiator

The core distinction of darkc0de/3.2test is its 'decensored' nature, achieved through the Heretic tool. This modification aims to provide a model with fewer content restrictions and a substantially reduced tendency to refuse prompts, making it suitable for use cases where the base model's refusal rates are prohibitive.

Use Cases

This model is particularly well-suited for applications requiring a powerful 24B parameter language model that can generate responses with fewer content filters and refusals than its original, more restrictive counterparts. Developers seeking a less constrained instruction-following model for text generation tasks may find this model beneficial.