Instructions to use bloomer010/Ling-3.0-flash-REAP288-73B-A5B-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 bloomer010/Ling-3.0-flash-REAP288-73B-A5B-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 bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M

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 bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M

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 bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF with Ollama:
```
ollama run hf.co/bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M

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": "bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF with Docker Model Runner:
```
docker model run hf.co/bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M
```

Lemonade

How to use bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ling-3.0-flash-REAP288-73B-A5B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bloomer010/Ling-3.0-flash-REAP288-73B-A5B-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 bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M

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 bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M

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 "bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF:Q4_K_M" \
  --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"

This is an experimental REAP.

Ling-3.0-flash REAP288 (73B total / 5.1B active) - GGUF

[288 of 512 routed experts kept per layer - 44% of experts pruned] from inclusionAI/Ling-3.0-flash (124B total / 5.1B active).

Method: one-shot REAP (Router-weighted Expert Activation Pruning) - experts scored by router-gate-value × output-L2-norm over calibration data, lowest-scoring deleted. No fine-tuning, no recovery training.

Calibration: 1M tokens, 50/25/25 ultrachat / wikitext / code

🎉 bailingmoe3 is supported in stock llama.cpp since PR #26608 (merged 2026-08-17, commit 3733366720). Any build from that commit onward loads these files directly.

🔔 2026-08-21: added reasoning_effort support (low = thinking off, high = on, default same). If you want reasoning_effort," re-download or override with chat_template.jinja.

Serving with experts in CPU RAM (attention on GPU, experts streamed from RAM):

llama-server -m Ling-3.0-flash-REAP288-71B-A5B-MXFP4.gguf   -ngl 99 -ot "ffn_.*_exps\.weight=CPU" --no-mmap -c 65536 --flash-attn on --jinja

Quants in this repo are all cut from a full-precision master: MXFP4, Q4_K_M, Q3_K_M, Q2_K

MXFP4 (experts MXFP4 / rest Q8_0) is the pick for CPU-offload serving.

Downloads last month: 1,023

GGUF

Model size

73B params

Architecture

bailingmoe3

Hardware compatibility

2-bit

3-bit

4-bit

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Model tree for bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF

Base model

inclusionAI/Ling-3.0-flash

Quantized

(46)

this model

Paper for bloomer010/Ling-3.0-flash-REAP288-73B-A5B-GGUF

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

Paper • 2510.13999 • Published Oct 15, 2025 • 23