Instructions to use shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6 with MLX:

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

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

Pi new

How to use shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6"

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": "shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6 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 "shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6"

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 shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6

Run Hermes

hermes

MLX LM

How to use shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Step-3.5-Flash REAP 128B-A11B — MLX Mixed 4/6-bit

MLX mixed-precision quantized version of lkevincc0/Step-3.5-Flash-REAP-128B-A11B for efficient local inference on Apple Silicon.

Quantization: Mixed 4/6-bit — v_proj and down_proj at 6-bit, all other weights at 4-bit (group size 64, affine mode)
Architecture: Step-3.5 SMoE — 45 layers, 173 routed experts (REAP-pruned), 8 active per token, shared expert
Parameters: 128B total, 11B active per token
Context: 262K tokens
Size: ~68 GB
Pruning: ~40% of experts removed via REAP (Router Expert Activation Pruning)

Usage

from mlx_lm import load, generate

model, tokenizer = load("shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6")
response = generate(model, tokenizer, prompt="Hello!", verbose=True)

Or with vMLX for native macOS inference.

About

Step-3.5-Flash is a large Mixture-of-Experts language model by StepFun AI. This variant was pruned by lkevincc0 using REAP (Router Expert Activation Pruning), reducing the expert count from the original to 173 while maintaining strong performance. The mixed-precision MLX quantization preserves higher fidelity on critical attention and feed-forward projections by using 6-bit for v_proj and down_proj layers.

Made for vMLX

This model was converted and optimized for vMLX — a free, open source macOS native MLX inference engine for Apple Silicon. Download vMLX to run this model locally with zero configuration.

Credits

Base model: stepfun-ai/Step-3.5-Flash by StepFun AI
REAP pruning: lkevincc0/Step-3.5-Flash-REAP-128B-A11B by lkevincc0
MLX conversion: vMLX — Run AI locally on Mac. No compromises.

Contact

For questions, issues, or collaboration: admin@vmlx.net

Downloads last month: 149

Safetensors

Model size

121B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit

Model tree for shieldstackllc/Step-3.5-Flash-REAP-128B-A11B-mlx-mixed-4-6

Base model

stepfun-ai/Step-3.5-Flash

Quantized

lkevincc0/Step-3.5-Flash-REAP-128B-A11B

Quantized

(4)

this model