Instructions to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-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 GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit

Run Hermes

hermes

MLX LM

How to use GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Ornstein-Hermes-3.6-27B SABER MLX 4-bit

This is an MLX 4-bit quantized conversion of GestaltLabs/Ornstein-Hermes-3.6-27b-SABER.

The source model is a SABER-edited release candidate of GestaltLabs/Ornstein-Hermes-3.6-27b, selected for its observed refusal/KLD tradeoff on the source evaluation set. See the source model card for the full methodology, evaluation details, attribution, and limitations.

Quantization

field	value
Format	MLX safetensors
Quantization	affine
Bits	4
Group size	64
Reported bits per weight	4.501
Uploaded storage	~15.2 GB

Use With MLX

Install mlx-lm, then load this repo directly:

pip install -U mlx-lm

mlx_lm.generate \
  --model GestaltLabs/Ornstein-Hermes-3.6-27b-SABER-MLX-4bit \
  --prompt "Explain quantum computing in simple terms."

Python:

from mlx_lm import load, generate

model, tokenizer = load("GestaltLabs/Ornstein-Hermes-3.6-27b-SABER-MLX-4bit")
response = generate(
    model,
    tokenizer,
    prompt="Explain quantum computing in simple terms.",
    max_tokens=256,
)
print(response)

For chat-style usage, apply the tokenizer chat template used by the source model before generation.

Notes

This repo contains only the MLX-converted weights and tokenizer assets.
Behavioral claims, evaluation numbers, and limitations are inherited from the source model.
The source model is a model-editing research artifact with dual-use implications.
The model inherits licensing considerations from the source and base model.

Source

Source model: GestaltLabs/Ornstein-Hermes-3.6-27b-SABER
Base model lineage: GestaltLabs/Ornstein-Hermes-3.6-27b

Downloads last month: 198

Safetensors

Model size

27B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Collection including GestaltLabs/Ornstein-Hermes-3.6-27B-SABER-MLX-4bit

Ornstein Series

Collection

Ornstein post-trained models: base, Hermes, SABER. GGUF + MLX. • 9 items • Updated 28 days ago • 1