qwen3.8 27b abliterated 3.69bpw 12GB MTP.gguf

Qwen 3

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released August 2026context N/A

A quantized, abliterated variant of Qwen3's 27B model, compressed to run within 12GB of memory using 3.69 bits-per-weight precision. The abliteration process removes refusal behaviors, making it more permissive in what it will discuss. Expect some capability loss compared to the full-precision original due to aggressive quantization.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multilingual

Strong

Creative Writing

Strong

Coding

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

qwen3.8 27b abliterated 3.69bpw 12GB MTP.gguf

Qwen 3

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released August 2026context N/A

A quantized, abliterated variant of Qwen3's 27B model, compressed to run within 12GB of memory using 3.69 bits-per-weight precision. The abliteration process removes refusal behaviors, making it more permissive in what it will discuss. Expect some capability loss compared to the full-precision original due to aggressive quantization.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multilingual

Strong

Creative Writing

Strong

Coding

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

AbliteratedA model variant where safety filters and refusal mechanisms have been removed, allowing it to respond to requests without built-in content restrictions.AbliterationA technique that removes or disables a model's built-in safety refusal mechanisms, allowing it to respond to a wider range of requests.Bits-Per-WeightA measure of how many bits of data are used to store each weight in a neural network; lower values mean more compression but potentially lower quality.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.Weight PrecisionThe number of bits used to represent each numerical value in a model's weights; lower precision (like 4-bit) uses less memory but may reduce accuracy.