Instructions to use TheCluster/Darwin-27B-Opus-MLX-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
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
- LM Studio
- Pi new
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 @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": "TheCluster/Darwin-27B-Opus-MLX-bf16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
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
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.
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Base model
Qwen/Qwen3.5-27B