Magistral-Small-2509-ultra-uncensored-heretic-v1 Overview
This model is a decensored variant of mistralai/Magistral-Small-2509, developed by llmfan46 using the Heretic v1.2.0 tool with the Arbitrary-Rank Ablation (ARA) method. The primary goal of this modification is to drastically reduce content refusals while maintaining the original model's performance and quality.
Key Differentiators & Performance
- Decensored Output: Achieves a significant reduction in refusals, with only 5/100 compared to the original model's 92/100, making it suitable for use cases requiring less restrictive content generation.
- Quality Preservation: Demonstrates a low KL divergence of 0.0274, indicating excellent preservation of the base model's coherence, reasoning ability, and overall quality despite decensoring.
- Reasoning Capabilities: Inherits and enhances the reasoning capabilities of the base Magistral Small 1.2, which is built upon Mistral Small 3.2 (2506) and includes SFT from Magistral Medium traces and RL.
- Multimodality: Supports vision inputs, allowing it to analyze images and reason based on visual content in addition to text.
- Multilingual Support: Capable of handling dozens of languages, including English, French, German, Japanese, Chinese, and many others.
- Context Window: Features a 128k context window, with good performance expected up to 40k tokens.
Usage & Features
- Thinking and Instruct Mode: Users can enable a thinking process (inside
[THINK]...[/THINK] tags) by using the provided SYSTEM_PROMPT.txt, or disable it for direct responses with other system prompts. - Deployment: The base Magistral Small is a 24B parameter model designed to be deployable locally, fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized. GGUF quantizations are available here.
When to Use This Model
- Uncensored Applications: Ideal for applications where content restrictions are undesirable or need to be minimized.
- Complex Reasoning Tasks: Suitable for tasks requiring long chains of reasoning, especially when combined with the thinking mode.
- Multimodal AI: Effective for scenarios involving both text and image analysis.
- Multilingual Applications: Can be used for tasks across a wide range of languages.