How to use from
Hermes Agent
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TheCluster/Qwen3.5-9B-Heretic-MLX-mxfp8"
Configure Hermes
# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default TheCluster/Qwen3.5-9B-Heretic-MLX-mxfp8
Run Hermes
hermes
Quick Links
A newer version of this model is available: TheCluster/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8

Qwen3.5-9B Heretic

Quality: quantized (mxfp8, naive, group size: 32)

This is an uncensored version of Qwen/Qwen3.5-9B, made using Heretic v1.2.0 with Magnitude-Preserving Orthogonal Ablation (MPOA)

Alternative fine-tuned version: TheCluster/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED-MLX-mxfp8

Sampling Parameters:

  • I suggest using the following sets of sampling parameters depending on the mode and task type:
    • Thinking mode for general tasks:
      temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
    • Instruct (or non-thinking) mode for general tasks:
      temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
    • Instruct (or non-thinking) mode for reasoning tasks:
      temperature=1.0, top_p=1.0, top_k=40, min_p=0.0, presence_penalty=2.0, repetition_penalty=1.0
  • For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

Source

This model was converted to MLX format from darkc0de/Qwen3.5-9B-heretic using mlx-vlm version 0.3.12.

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