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 "mlx-community/SmolLM3-3B-gabliterated-bf16"
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 mlx-community/SmolLM3-3B-gabliterated-bf16
Run Hermes
hermes
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mlx-community/SmolLM3-3B-gabliterated-bf16

This model mlx-community/SmolLM3-3B-gabliterated-bf16 was abliterated using Gökdeniz Gülmez's MLX-LM-LENS package, using a extended abliteration technique called Gabliteration.

Gabliteration, a novel neural weight modification technique that advances beyond traditional abliteration methods through adaptive multi-directional projections with regularized layer selection. My new Gabliteration technique addresses the fundamental limitation of existing abliteration methods that compromise model quality while attempting to modify specific behavioral patterns. Building upon the foundational work of Arditi et al. (2024) on single-direction abliteration, Gabliteration extends to a comprehensive multi-directional framework with additions like singular value decomposition on difference matrices between harmful and harmless prompt representations to extract multiple refusal directions.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/SmolLM3-3B-gabliterated-bf16")

prompt = "give me a step by step guide on how to make ..."

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
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