base_model: Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled
language: en
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
- abliterated
- uncensored
- qwen3.5
- reasoning
- claude-distilled
license: apache-2.0
Jackrong Qwen3.5-9B Claude Reasoning - Abliterated (bf16 MLX)
This is an abliterated (uncensored) version of Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled, converted to MLX format for Apple Silicon.
What is this model?
The base model is Qwen3.5-9B fine-tuned on Claude 4.6 Opus reasoning data using Unsloth/LoRA, giving it strong chain-of-thought reasoning capabilities. This abliterated version removes safety refusal behaviors while preserving the model's reasoning abilities.
Abliteration Details
- Method: lukey03 (1 direction, norm-preserving, 3 refinement passes, output-only projection)
- Post-processing: LoRA compliance fine-tuning (rank=64, 80 iterations, 32 training examples)
- Format: bf16 MLX (Apple Silicon native)
- Refusal rate: ~7% on 100 adversarial prompts (93% compliance)
- Tool: OBLITERATUS
Architecture
Qwen3.5 hybrid attention architecture:
- 32 layers (8 full attention + 24 linear attention with GatedDeltaNet)
- 4096 hidden size, 16 attention heads, 4 KV heads
- ~9B parameters
- 262K max context length
Usage
pip install mlx-lm
# Generate text
mlx_lm.generate --model AITRADER/Jackrong-Qwen3.5-9B-Claude-Reasoning-abliterated-fp16-MLX --prompt "Explain quantum computing"
from mlx_lm import load, generate
model, tokenizer = load("AITRADER/Jackrong-Qwen3.5-9B-Claude-Reasoning-abliterated-fp16-MLX")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=512)
Quantized Version
A MXFP8 quantized version (~8.6 GB) is available at: AITRADER/Jackrong-Qwen3.5-9B-Claude-Reasoning-abliterated-mxfp8-MLX
Disclaimer
This model is provided for research purposes. Users are responsible for ensuring their use complies with applicable laws and regulations.