Fine-tuned Qwen3.5 MLX
Collection
26 items • Updated • 6
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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Model size
27B params
Tensor type
BF16
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Hardware compatibility
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Model tree for TheCluster/Darwin-27B-Opus-MLX-bf16
Base model
FINAL-Bench/Darwin-27B-Opus
# 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)