reaperdoesntknow/DiStil-Qwen3-1.7B-uncensored
DiStil-Qwen3-1.7B-uncensored is a 1.7 billion parameter Qwen3ForCausalLM model developed by Convergent Intelligence LLC: Research Division. It is a distillation of Qwen3, fine-tuned with uncensored SFT data to remove alignment-imposed refusal behaviors while retaining reasoning and generation capabilities. This model features a 40,960 token context length and is designed to respond directly to prompts without filtering through safety heuristics, making it suitable for technical, analytical, and research queries.
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Model Overview
DiStil-Qwen3-1.7B-uncensored is a 1.7 billion effective parameter model from Convergent Intelligence LLC: Research Division, built on the Qwen3ForCausalLM architecture. It features a substantial 40,960 token context length, 28 layers, and a hidden size of 2048. This model is a key component in a distillation chain, with a subsequent refinement available as Disctil-Qwen3-1.7B.
Key Capabilities & Differentiators
- Uncensored Response Generation: The model was fine-tuned using uncensored SFT data to eliminate alignment-imposed refusal behaviors, ensuring it responds directly to prompts without filtering for safety heuristics.
- Preserved Reasoning: Despite the uncensored fine-tuning, the model aims to preserve the base Qwen3's core reasoning and generation capabilities.
- Distillation Chain: This model is part of a larger distillation effort, leveraging "Discrepancy Calculus" (DISC) — a measure-theoretic framework for transferring capabilities from larger teacher models.
- Pure SFT Intervention: Training involved supervised fine-tuning (SFT) using TRL on uncensored instruction data, without architectural modifications, focusing solely on shifting the model's response distribution.
Ideal Use Cases
- Applications requiring direct, unfiltered responses to technical, analytical, or research queries.
- Scenarios where avoiding refusal behaviors from alignment training is critical.
- As a base model for further refinement in distillation chains or specialized fine-tuning tasks.