This repository hosts uncensored and efficiency-focused builds of DeepSeek-R1-Distill-Qwen-7B, intended for users who require direct model behavior, strong reasoning, and full local control without aggressive automated filtering.
The model is suitable for advanced experimentation, private deployments, and research scenarios where transparency and flexibility are prioritized.
Model Overview
Model Name: DeepSeek-R1-Distill-Qwen-7B-Uncensored
Base Model: DeepSeek-R1-Distill-Qwen-7B
Architecture: Decoder-only Transformer
Parameter Count: ~7B
Modalities: Text
Context Length: Up to 32K tokens (runtime dependent)
This release is designed for users who want minimal behavioral constraints while preserving the structured reasoning and instruction-following strengths of the DeepSeek-R1 distillation.
Key objectives include:
Predictable, direct responses without heavy content suppression
Strong multi-step reasoning and analytical depth
Compatibility with local and offline inference setups
A solid foundation for further alignment, fine-tuning, or research
This is not a consumer-safety-aligned assistant and is intended for controlled environments.
Quantized Variants (GGUF)
To support a wide range of hardware, multiple GGUF quantization levels are provided.
Q2_K (2-bit)
Extremely small memory footprint
Intended for experimentation or extreme hardware constraints
Severe degradation in reasoning and instruction accuracy
Q3_K_M (3-bit)
Slight improvement over 2-bit
Lightweight and fast
Limited suitability for complex reasoning tasks
Q4_K_M (4-bit)
Strong efficiency-to-quality tradeoff
Works well on CPUs and low-VRAM GPUs
Suitable for general chat and exploratory reasoning
Q5_K_M (5-bit)
Recommended default for most users
Retains most reasoning and instruction-following ability
Balanced memory usage and output quality
Q6_K (6-bit)
Higher reasoning fidelity
Increased memory requirements
Better performance on long or complex prompts
Q8_0 (8-bit)
Near full-precision behavior
Highest quality quantized variant
Best choice when memory is not a limiting factor
Output quality depends heavily on context length, sampling parameters, and inference backend.
Prompting Format
The model performs best with a structured chat format:
<|system|>
High-level instructions or behavioral guidance
<|user|>
User prompt
<|assistant|>
Clear system messages are recommended to guide tone, verbosity, and task focus.
Suggested Settings
Temperature: 0.6 – 0.8 for analytical tasks
Use Q5_K_M or higher for reasoning-heavy prompts
Avoid ultra-low-bit quantizations for long-context analysis
Capabilities
Strong logical and mathematical reasoning
Effective multi-step analysis and planning
Clear instruction-following behavior
Suitable for research into reasoning and alignment
Performs well in uncensored local deployments
Maintains coherence over extended conversations
Recommended Use Cases
Local reasoning assistants
Research and alignment studies
Offline analysis and experimentation
Advanced prompt engineering workflows
Private deployments requiring full user control
Important Notes
This model intentionally avoids strong automated moderation
Users are responsible for ensuring lawful and ethical usage
Not recommended for unsupervised or public-facing applications
Quantized variants may hallucinate more than full-precision models
Always evaluate outputs in the context of your intended application.
Acknowledgements
DeepSeek AI for releasing the DeepSeek-R1 model family
Qwen team for the underlying architecture contributions
The llama.cpp and GGUF ecosystem for enabling efficient local inference
Open-source contributors supporting transparent LLM research
Contact
For issues related to quantization files or repository content, please open an issue in this repository.
