Instructions to use TheCluster/Darwin-27B-Opus-MLX-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheCluster/Darwin-27B-Opus-MLX-bf16 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("TheCluster/Darwin-27B-Opus-MLX-bf16")
config = load_config("TheCluster/Darwin-27B-Opus-MLX-bf16")

# 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 TheCluster/Darwin-27B-Opus-MLX-bf16 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TheCluster/Darwin-27B-Opus-MLX-bf16"

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": "TheCluster/Darwin-27B-Opus-MLX-bf16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use TheCluster/Darwin-27B-Opus-MLX-bf16 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 "TheCluster/Darwin-27B-Opus-MLX-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 TheCluster/Darwin-27B-Opus-MLX-bf16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TheCluster/Darwin-27B-Opus-MLX-bf16 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TheCluster/Darwin-27B-Opus-MLX-bf16"

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 "TheCluster/Darwin-27B-Opus-MLX-bf16" \
  --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"

Darwin-27B-Opus-MLX-bf16 / README.md

TheCluster

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metadata

license: apache-2.0
base_model:
  - FINAL-Bench/Darwin-27B-Opus
pipeline_tag: image-text-to-text
language:
  - en
  - zh
  - ru
  - es
  - fr
  - it
  - ja
  - ko
  - af
  - de
  - ar
  - tr
  - is
  - pl
  - sw
  - sv
  - nl
  - he
  - id
  - uk
  - fa
  - pa
  - pt
  - ms
  - fi
  - el
library_name: mlx

Darwin-27B-Opus

Quality: original (bfloat16)

Darwin-27B-Opus is a 27-billion-parameter language model produced entirely through evolutionary crossbreeding of pretrained models, requiring zero additional training, zero data, and a single GPU. On the GPQA Diamond benchmark — a graduate-level scientific reasoning evaluation comprising 198 expert-crafted questions in physics, chemistry, and biology — Darwin-27B-Opus achieves 86.9%, surpassing its progenitor Qwen3.5-27B (85.5%) by +1.4 percentage points and securing 5th place on the HuggingFace GPQA leaderboard.

Model Specifications


Architecture	Qwen3.5 Dense (GatedDeltaNet)
Parameters	27B
Hidden Size	4096
Intermediate Size	17408
Layers	64
Context Length	262,144 (extensible to 1M via YaRN)
Precision	BF16
Languages	201
Thinking Mode	Enabled

Parent Models

Role	Model	Contribution
Father (Structure)	Qwen/Qwen3.5-27B	Foundation architecture, native reasoning, 201-language support
Mother (Knowledge)	Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled	Claude 4.6 Opus structured reasoning patterns via SFT distillation

Both parents share identical architecture: hidden_size=4096, intermediate_size=17408, 64 layers — ensuring 100% structural compatibility for FFN crossbreeding.

Source

This model was converted to MLX format from FINAL-Bench/Darwin-27B-Opus using mlx-vlm version 0.4.4.