---

license: apache-2.0

base_model:

• huihui-ai/DeepSeek-R1-Distill-Qwen-32B-abliterated

tags:

• deepseek
• deepseek-r1
• qwen
• text-generation
• GGUF
• abliterated
• uncensored
• reasoning

---

Why this repository?

Unlike incomplete GGUF uploads, this repository provides the full 11-quantization spectrum (from high-precision F16 down to lightweight Q2_K) of the abliterated DeepSeek-R1-Distill-Qwen-32B model.

Choose the exact fit for your VRAM/RAM constraints without sacrificing reasoning capabilities.

DeepSeek-R1-Distill-Qwen-32B-abliterated - GGUF

This is the integral and complete collection of quantizations in GGUF format for the DeepSeek-R1-Distill-Qwen-32B-abliterated model, prepared locally for use with llama.cpp, Ollama, LM Studio, or Text-Generation-WebUI.

This model combines advanced reasoning distillation (deep logical thinking capability via <think> tags) based on Alibaba's powerful Qwen-32B model, but processed with abliteration techniques to root out artificial system censorship filters and blocks, responding without restrictions.

📋 Available Files (Complete Collection Without Splits)

| File | Est. Size | BPW (Bits per Weight) | Recommended Usage Profile |

| :--- | :---: | :---: | :--- |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-F16.gguf | ~65.6 GB | 16.00 | Complete Base Template. Absolute fidelity of floating-point weights. |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-Q8_0.gguf | ~34.8 GB | 8.50 | Identical quality to the original, ideal for maximizing performance on local hardware with high-end GPUs. |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-Q6_K.gguf | ~27.2 GB | 6.59 | Extremely high retention of the logical reasoning tree with an optimized weight. |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-Q5_K_M.gguf | ~23.4 GB | 5.69 | Recommended Sweet Spot. Keeps deep thinking coherence intact while critically reducing weight. |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-Q5_K_S.gguf | ~22.8 GB | 5.54 | Compact 5-bit variant. |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-Q4_K_M.gguf | ~19.9 GB | 4.85 | The Most Wanted. Optimal balance to run 32B reasoning inferences on advanced home setups. |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-Q4_K_S.gguf | ~18.8 GB | 4.58 | Compact 4-bit variant to accelerate tokens-per-second speed. |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-Q3_K_L.gguf | ~16.5 GB | 4.01 | Medium-high 3-bit compression. Retains basic reasoning capabilities. |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-Q3_K_M.gguf | ~15.1 GB | 3.66 | Intermediate 3-bit variant. |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-Q3_K_S.gguf | ~14.2 GB | 3.44 | Lightweight 3-bit variant. |

| DeepSeek-R1-Distill-Qwen-32B-abliterated-Q2_K.gguf | ~12.1 GB | 2.90 | Extreme Compression. May experience minor issues in the internal structure of thinking tags. For experimental development only. |

Note: Sizes are initial baseline estimates based on the model's native weight in safetensors (32.8B parameters); verifying the final size on disk after local compilation is recommended.

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💡 Highlighted Usage

Since this is a distilled deep reasoning model, it is recommended to use a clean prompt format that respects the native chain-of-thought generation in your terminal:

./llama-cli -m DeepSeek-R1-Distill-Qwen-32B-abliterated-Q4_K_M.gguf -n 2048 -p "How does elliptic curve cryptography work?"

> Usage Tip: If you notice the model skipping the <think> tag or attempting an initial refusal given an ambiguous prompt, you can guide the inference by providing a few-shot example or pre-filling the assistant's response with an opened <think> tag to force the restriction-free reasoning chain.

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⚖️ Disclaimer

This model is open-access and lacks artificial safety filters due to the experimental abliteration process. The generated content and resulting logical reflections are the sole responsibility of the individual running the local inference.

🔒 Verificación de Integridad Sha256sum (SHA-256)

Para asegurarte de que los archivos de gran tamaño no se hayan corrompido durante la descarga, podés verificar su integridad utilizando el archivo oficial SHA256SUMS.txt provisto en este repositorio.

En Linux / macOS:

Abre una terminal en la carpeta donde descargaste el modelo y el archivo de hashes, y ejecuta(EJEMPLO):

grep "DeepSeek-R1-Distill-Qwen-32B-abliterated-Q4_K_M.gguf" SHA256SUMS.txt | sha256sum -c

Resultado Esperado:

• DeepSeek-R1-Distill-Qwen-32B-abliterated-Q4_K_M.gguf: La suma coincide (o OK) (¡Descarga perfecta!)

Resultado Negativo:

• DeepSeek-R1-Distill-Qwen-32B-abliterated-Q4_K_M.gguf: "La suma NO coincide (¡Falla!)

La descarga falló o está incompleta. Se recomienda volver a descargar ese archivo específico.

Nota: Si vas a verificar otro tamaño (como el Q5_K_M o el Q8_0), simplemente reemplaza el nombre del archivo dentro de las comillas del comando.

Credits

• Base Distilled Model: deepseek-ai / Qwen (Alibaba Cloud)
• Abliteration: huihui-ai
• Complete GGUF Quantizations: Thaurock