Fine-tuned Qwen3.5 MLX
Collection
26 items • Updated • 5
How to use TheCluster/Darwin-35B-A3B-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-35B-A3B-Opus-MLX-bf16")
config = load_config("TheCluster/Darwin-35B-A3B-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)How to use TheCluster/Darwin-35B-A3B-Opus-MLX-bf16 with Pi:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TheCluster/Darwin-35B-A3B-Opus-MLX-bf16"
# 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-35B-A3B-Opus-MLX-bf16"
}
]
}
}
}# Start Pi in your project directory: pi
How to use TheCluster/Darwin-35B-A3B-Opus-MLX-bf16 with Hermes Agent:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TheCluster/Darwin-35B-A3B-Opus-MLX-bf16"
# 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-35B-A3B-Opus-MLX-bf16
hermes
Quality: original (bfloat16)
| Architecture | Qwen3.5 MoE (Gated DeltaNet + MoE) |
| Total Parameters | 35B |
| Active Parameters | 3B per forward pass |
| Layers | 40 |
| Layout | 10 x (3 x GDN-MoE + 1 x Attention-MoE) |
| Experts | 256 (8 routed + 1 shared active) |
| Context Length | 262,144 native |
| Languages | 201 |
| Multimodal | Image and Video |
| License | Apache 2.0 |
Both parents share the identical Qwen3.5-35B-A3B architecture (40 layers, 256 experts, GDN+MoE hybrid). The Mother is a LoRA SFT on the same base — not a different architecture. "Text-only" refers to the training data (Claude 4.6 Opus reasoning chains), not the model structure.
| Role | Model | Architecture | Training |
|---|---|---|---|
| Father | Qwen/Qwen3.5-35B-A3B | Qwen3.5-35B-A3B | Original pre-training + RLHF |
| Mother | Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled | Qwen3.5-35B-A3B (same) | LoRA SFT with text-only Claude reasoning chains |
This model was converted to MLX format from FINAL-Bench/Darwin-35B-A3B-Opus using mlx-vlm version 0.4.4.
Quantized
Base model
FINAL-Bench/Darwin-35B-A3B-Opus