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Model: Andycurrent/Qwen3-0.6B-heretic-abliterated-uncensored-GGUF Source: Original Platform
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README.md
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-0.6B
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tags:
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- uncensored
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- abliterated
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- text-generation
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- instruction-tuned
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- lightweight
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- qwen3
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# Qwen3 – 0.6B Heretic Abliterated Uncensored
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This repository hosts Qwen3 – 0.6B Heretic Abliterated Uncensored, a lightweight 0.6 billion–parameter instruction-tuned model derived from Alibaba’s Qwen3-0.6B base model.
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This variant is modified using the Heretic abliteration method to reduce refusal behavior and increase direct-answer generation while preserving the original conversational and reasoning capabilities of the Qwen3 architecture.
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---
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## Model Overview
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- **Model Name**: Qwen3 – 0.6B Heretic Abliterated Uncensored
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- **Parameter Count**: 0.6 Billion (0.6B)
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- **Base Architecture**: Qwen3
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- **Base Model**: Qwen/Qwen3-0.6B
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- **Model Type**: Instruction-Tuned Causal Language Model
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- **Context Length**: Inherits base model context window
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- **Primary Language**: English
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- **License**: Apache 2.0 (inherits from base model)
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- **Maintainer / Publisher**: DavidAU
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---
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## What Is This Model?
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This model is a modified derivative of Qwen3-0.6B configured for:
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- Reduced refusal behavior
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- More direct instruction responses
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- Lightweight uncensored conversational generation
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- Fast inference on low-resource hardware
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- Experimental unrestricted assistant behavior
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The “Heretic Abliterated Uncensored” configuration emphasizes:
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- Minimal alignment restrictions
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- Improved response openness
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- Reduced safety filtering
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- Better instruction compliance in unrestricted prompts
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This model is intended primarily for experimentation, research, local inference, and creative applications.
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---
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## Key Features & Capabilities
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### Core Strengths
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- Extremely lightweight deployment footprint
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- Fast token generation speed
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- Low VRAM usage
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- Good instruction-following behavior
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- Compatible with local inference stacks
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- Suitable for edge and CPU inference
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### Performance Characteristics
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- Optimized for fast short-to-medium generations
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- Responsive for local assistant deployments
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- Practical for experimentation with uncensored workflows
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- Useful for lightweight agent systems and testing environments
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---
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## Intended Use Cases
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- Local AI assistant deployment
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- Creative writing and storytelling
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- Prompt engineering experimentation
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- Research into alignment and refusal behavior
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- Tool-augmented agents
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- Low-resource inference environments
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- Educational experimentation with lightweight LLMs
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---
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## Chat Template & Prompt Format
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This model follows the Qwen3 instruction/chat format.
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For best results:
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- Use structured role-based prompts
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- Provide explicit system instructions
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- Avoid mixing incompatible chat templates
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- Use Qwen-compatible inference frameworks
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---
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## Hardware & Deployment Notes
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Due to its compact 0.6B parameter size:
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- Runs efficiently on low-end GPUs
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- Suitable for CPU inference
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- Practical for laptops and edge devices
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- Works well with quantized GGUF deployments
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- Compatible with llama.cpp, vLLM, Ollama, LM Studio, and similar frameworks
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Quantized variants such as GGUF, GPTQ, AWQ, and IQ quants may significantly reduce memory usage while preserving good inference quality.
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---
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## Alignment & Safety Notice
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This is an uncensored / abliterated derivative configuration.
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- Reduced refusal behavior compared to standard aligned models
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- Safety filtering may be significantly reduced
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- Users are responsible for deployment safeguards and moderation
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- Outputs may include unrestricted or unsafe generations
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Use responsibly and comply with all applicable laws and platform policies.
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---
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## Abliteration Details
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Abliterated/uncensored using the Heretic method.
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Reported repository metrics include:
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- Refusals: 6/100
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- KL Divergence: 0.00
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- Original base model refusal rate: 49/100
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These metrics are repository-provided experimental measurements and may vary depending on prompts and inference settings.
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---
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## License & Usage Notes
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This model inherits the **Apache 2.0 License** from its base model (*Qwen/Qwen3-0.6B*).
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- Users must comply with the original Qwen licensing terms
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- This repository does not replace or modify the upstream license
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- Users are responsible for safe and lawful deployment
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---
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## Acknowledgements
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- Alibaba Qwen team for the Qwen3 architecture
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- DavidAU for the Heretic abliterated derivative work
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- The Hugging Face ecosystem
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- Open-source inference and quantization communities
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---
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## Community & Support
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- Use the Hugging Face Discussions tab for questions and feedback
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- Community benchmarks and quantization experiments are welcome
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- GGUF and IQ quantized variants are available through community repositories
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