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/Qwen3.6-27B-MLX-mixed-9.4bit"
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/Qwen3.6-27B-MLX-mixed-9.4bit
Run Hermes
hermes
Quick Links

Qwen3.6-27B

Quality: quantized (mixed quants per tensor, group size: 32, 9.450 bpw).

Most tensors use 8-bit affine quantization with a group size 32; some important tensors are saved in bf16.

Recommended settings

  1. Sampling Parameters:
    • The developers 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
      • Thinking mode for precise coding tasks (e.g., WebDev):
        temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, 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 Qwen/Qwen3.6-27B using mlx-vlm version 0.4.4.

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