Instructions to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./llama-cli -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Use Docker

docker model run hf.co/patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

LM Studio
Jan
Ollama
How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with Ollama:
```
ollama run hf.co/patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
```
Unsloth Desktop

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with Docker Model Runner:
```
docker model run hf.co/patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
```

Lemonade

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Run and chat with the model

lemonade run user.MiMo-V2.6-Flash-REAP50-GGUF-MXFP4_MOE

List all available models

lemonade list

Hermes Agent

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

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 patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

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 "patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MiMo-V2.6-Flash-REAP50-GGUF / README.md

patrickbdevaney

Upload README.md with huggingface_hub

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	---
	license: mit
	base_model:
	- XiaomiMiMo/MiMo-V2.6-Flash
	- patrickbdevaney/MiMo-V2.6-Flash-REAP50
	tags:
	- gguf
	- llama.cpp
	- reap
	- hope
	- moe
	- pruned
	- multimodal
	- vision
	- audio
	- mtp
	---

	# Xiaomi MiMo-V2.6-Flash REAP-50 — GGUF

	Official GGUF quantisations of MiMo-V2.6-Flash-REAP50, a 50% routed-expert pruned checkpoint of `XiaomiMiMo/MiMo-V2.6-Flash` created with [REAP](https://github.com/CerebrasResearch/reap) and HOPE second-order saliency pruning.

	* Base HF Checkpoint: [patrickbdevaney/MiMo-V2.6-Flash-REAP50](https://huggingface.co/patrickbdevaney/MiMo-V2.6-Flash-REAP50)
	* Experts Retained: 128 of 256 routed experts per layer across 47 MoE layers (1 dense layer, 47 MoE layers).
	* Base Architecture: Native packed MXFP4 (`U8`, block size 32) experts with unquantized pure BF16 attention and embeddings.
	* Towers Included: Vision & Audio multimodal projectors (`mmproj`) and Multi-Token Prediction speculative draft heads (`mtp`).

	---

	## Quantization Ladder

	\| Filename \| Quant Type \| Size \| Description \| Recommended VRAM / RAM \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| `MiMo-V2.6-Flash-REAP50-MXFP4_MOE.gguf` \| MXFP4_MOE \| 86.06 GiB \| Flagship: 1-to-1 native packed MXFP4 experts (32 blk) + BF16 attention/trunk. Exact bit-level fidelity to REAP base. \| 96 GiB+ / 1x 128GB Thor or 2x 48GB \|
	\| `MiMo-V2.6-Flash-REAP50-Q2_K.gguf` \| Q2_K \| 61.64 GiB \| Optimal Hybrid MoE: sensitive down-projections kept in native MXFP4, gate/up in Q2_K, trunk in Q8_0 (~3.36 BPW). \| 64 GiB+ / 3x 24GB GPUs (72GB) or Mac 64-96GB \|

	### Supporting Towers (Vision, Audio & MTP)

	\| Filename \| Size \| Description \|
	\| :--- \| :--- \| :--- \|
	\| `mmproj-MiMo-V2.6-Flash-REAP50-BF16.gguf` \| 2.56 GiB \| Multimodal projector (Vision + Audio) in BF16 \|
	\| `mmproj-MiMo-V2.6-Flash-REAP50-Q8_0.gguf` \| 1.46 GiB \| Multimodal projector (Vision + Audio) quantized to Q8_0 \|
	\| `mtp-MiMo-V2.6-Flash-REAP50-BF16.gguf` \| 4.17 GiB \| Multi-Token Prediction (MTP) draft head (3 next-n layers) in BF16 \|
	\| `mtp-MiMo-V2.6-Flash-REAP50-Q8_0.gguf` \| 2.22 GiB \| Multi-Token Prediction (MTP) draft head (3 next-n layers) in Q8_0 \|

	---

	## Key Features

	1. Native MXFP4 MoE Preservation:
	In the base model, 92.9% of weights are stored as native packed `mxfp4` (32 block size). Our GGUF converter natively repacks these blocks directly into `GGMLQuantizationType.MXFP4`, avoiding costly lossy dequantization cycles while preserving exact native numerical precision.

	2. Multimodal Projectors (`mmproj`):
	Xiaomi MiMo-V2.6-Flash incorporates both visual and audio processing towers:
	- Vision encoder (28-layer ViT, 560px patch representation)
	- Audio tokenizer / RVQ speech representations
	Both are packed into standard GGUF multimodal projectors (`mmproj--BF16.gguf` and `mmproj--Q8_0.gguf`) compatible with `llama.cpp`'s multimodal pipeline.

	3. Multi-Token Prediction (`mtp`):
	MiMo-V2.6-Flash includes 3 trained MTP layers for speculative decoding. We ship standalone MTP draft models (`mtp--BF16.gguf` and `mtp--Q8_0.gguf`) that can be loaded alongside the trunk model with `--draft-model` to accelerate generation.

	---

	## Running with llama.cpp

	### 1. Standard Text Inference (Optimal Hybrid Q2_K)
	```bash
	./llama-cli \
	-m MiMo-V2.6-Flash-REAP50-Q2_K.gguf \
	-p "You are MiMo, an AI assistant developed by Xiaomi. Explain how MoE expert pruning works:" \
	-n 512 --temp 0.6
	```

	Or run the flagship bit-for-bit native MXFP4 checkpoint:
	```bash
	./llama-cli \
	-m MiMo-V2.6-Flash-REAP50-MXFP4_MOE.gguf \
	-p "You are MiMo, an AI assistant developed by Xiaomi. Explain how MoE expert pruning works:" \
	-n 512 --temp 0.6
	```

	### 2. Speculative Decoding with MTP Draft Head
	```bash
	./llama-cli \
	-m MiMo-V2.6-Flash-REAP50-Q2_K.gguf \
	--draft-model mtp-MiMo-V2.6-Flash-REAP50-Q8_0.gguf \
	-p "Explain quantum teleportation in detail:" \
	-n 512
	```

	### 3. Multimodal Inference (Vision & Audio)
	```bash
	./llama-cli \
	-m MiMo-V2.6-Flash-REAP50-Q2_K.gguf \
	--mmproj mmproj-MiMo-V2.6-Flash-REAP50-Q8_0.gguf \
	--image input.jpg \
	-p "Describe the contents of this image in detail."
	```

	### 4. OpenAI-Compatible API Server
	```bash
	./llama-server \
	-m MiMo-V2.6-Flash-REAP50-Q2_K.gguf \
	--mmproj mmproj-MiMo-V2.6-Flash-REAP50-Q8_0.gguf \
	--port 8080 \
	-ngl 99
	```

	---

	## Background & Pruning Method

	Pruned using HOPE (Higher-Order Pruning of Experts) over a diverse calibration corpus spanning code, math, conversational text, and multimodal reasoning tasks. Rather than relying solely on first-order activation frequencies, HOPE accounts for inter-expert interaction terms:
	$$\Delta \mathcal{L} \approx \sum_{i} g_i^T \Delta w_i + \frac{1}{2} \sum_{i,j} \Delta w_i^T H_{ij} \Delta w_j$$
	By computing cross-expert Hessian blocks during the calibration pass, 128 experts per layer were optimally selected to minimize perplexity loss under 50% parameter reduction.

	---
	Created by [patrickbdevaney](https://huggingface.co/patrickbdevaney).