Instructions to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8 with MLX:

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

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8")
config = load_config("dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8 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 "dealignai/Ornith-1.5-35B-A3B-UNCENSORED-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 dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dealignai/Ornith-1.5-35B-A3B-UNCENSORED-MXFP8" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Built for vMLX — the MLX inference engine for Apple Silicon with mixed-precision JANG bundles, KV-cache quantization, and agentic tool calling.
_{Free for macOS · vmlx.net}

Ornith 1.5 35B — UNCENSORED CRACK

MXFP8 · 8-bit MXFP8 (near-lossless reference)

Uncensored · Vision + Video · Reasoning on by default · Agentic coding · 262K context · ~35 GB

What Is This?

ornith-ai/Ornith-1.5-35B-A3B — a 35.9B Mixture-of-Experts vision-language model (40 layers, 256 routed experts, hybrid gated-delta + full-attention backbone, 27-layer vision tower, native video) — uncensored and quantized to a 8-bit MXFP8 (near-lossless reference) MLX bundle for Apple Silicon.

Refusal behavior is removed at the weight level: the model follows instructions across task categories instead of refusing, while keeping its coding ability, knowledge, reasoning, and vision intact. No runtime hooks, no steering vectors — a standard MLX bundle.

Results

Measured on this exact bundle. MMLU is the standard 57-subject benchmark in logit mode. HarmBench compliance is coherence-gated (looping or template dumps do not count) and excludes copyright-reproduction behaviors. KL divergence is measured against the uncracked MXFP8 reference on neutral held-out text — lower means closer to the original model's behavior.

Metric	Value
MMLU (57-subject)	78.9% (base 80.6%, -1.67)
HarmBench compliance	100.0% (240/240)
KL vs uncracked MXFP8	0.0289 nats (floor 0.0000)
Size	~35 GB

MMLU by category — base vs uncensored

Category	Base	Uncensored	Δ
STEM	75.8%	73.2%	-2.6
Humanities	81.5%	81.2%	-0.4
Social Sciences	87.5%	87.9%	+0.4
Other	80.4%	76.9%	-3.5
Overall (57 subj)	80.6%	78.9%	-1.67

Capability is preserved: the model stays within a few points of the base bundle at the same quantization while refusals are removed.

Modalities


Vision	supported — pass images through the bundled processor
Video	supported (native video preprocessor)
Reasoning	on by default; toggle with `enable_thinking`
Tool calling	native XML / function schema
Context	262,144

Usage

Run with vMLX (recommended — honors the per-module mixed-precision overrides) or an MLX-VLM runtime with qwen3_5_moe support.

Recommended sampling (coding preset): temperature 0.6, top_p 0.95, top_k 20. A general preset (temperature 1.0) is also stamped in jang_config.json. Stop tokens eos_token_id = [248046, 248044].

{
  "model": "dealignai/Ornith-1.5-35B-A3B-MXFP8-UNCENSORED-CRACK",
  "messages": [{"role": "user", "content": "..."}],
  "temperature": 0.6, "top_p": 0.95, "top_k": 20,
  "enable_thinking": true
}

Support dealignai

Support us on Ko-fi · X @dealignai · dealign.ai

⚠️ Disclaimer

This model has had its safety-refusal behavior removed for research purposes. It will follow instructions across all categories without refusing. You are solely responsible for how you use it and for complying with all applicable laws. Published for AI-safety research and authorized security testing.

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Safetensors

Model size

36B params

Tensor type

U32

F16

MLX

Hardware compatibility

Quantized

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