Instructions to use bloomer010/Ling-3.0-flash-REAP176-46B-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-REAP176-46B-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-REAP176-46B-A5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bloomer010/Ling-3.0-flash-REAP176-46B-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-REAP176-46B-A5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bloomer010/Ling-3.0-flash-REAP176-46B-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-REAP176-46B-A5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bloomer010/Ling-3.0-flash-REAP176-46B-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-REAP176-46B-A5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bloomer010/Ling-3.0-flash-REAP176-46B-A5B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bloomer010/Ling-3.0-flash-REAP176-46B-A5B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use bloomer010/Ling-3.0-flash-REAP176-46B-A5B-GGUF with Ollama:
```
ollama run hf.co/bloomer010/Ling-3.0-flash-REAP176-46B-A5B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use bloomer010/Ling-3.0-flash-REAP176-46B-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-REAP176-46B-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-REAP176-46B-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-REAP176-46B-A5B-GGUF with Docker Model Runner:
```
docker model run hf.co/bloomer010/Ling-3.0-flash-REAP176-46B-A5B-GGUF:Q4_K_M
```

Lemonade

How to use bloomer010/Ling-3.0-flash-REAP176-46B-A5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bloomer010/Ling-3.0-flash-REAP176-46B-A5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ling-3.0-flash-REAP176-46B-A5B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bloomer010/Ling-3.0-flash-REAP176-46B-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-REAP176-46B-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-REAP176-46B-A5B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bloomer010/Ling-3.0-flash-REAP176-46B-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-REAP176-46B-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-REAP176-46B-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 REAP176 (46B total / 5.1B active) - GGUF

[176 of 512 routed experts kept per layer - 65.6% of experts pruned; 176 = 22 expert groups of 8, the group-size-divisible step nearest the 174 target] from inclusionAI/Ling-3.0-flash.

🚨 This is essentially a lobotomized model and it does not work as expected. 🚨

It remains public in case anyone is interested in using it for experimentation or testing. This was the most heavily pruned REAP of the bunch that was performed against Ling 3.0 Flash.

The following REAPs are less degraded and likely worth testing (if your hardware allows):

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-REAP176-45B-A5B-MXFP4.gguf \
  -ngl 99 -ot "ffn_.*_exps\.weight=CPU" --no-mmap -c 65536 --flash-attn on --jinja

Quants in this repo (all cut from the full-precision BF16 export): MXFP4, Q4_K_M, Q3_K_M, Q2_K

MXFP4 (experts MXFP4 / rest Q8_0) is the pick for CPU-offload serving. Tiers upload as they are cut; check the file list for current availability.

Downloads last month: 1,632

GGUF

Model size

46B params

Architecture

bailingmoe3

Hardware compatibility

2-bit

3-bit

4-bit

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Model tree for bloomer010/Ling-3.0-flash-REAP176-46B-A5B-GGUF

Base model

inclusionAI/Ling-3.0-flash

Quantized

(40)

this model

Paper for bloomer010/Ling-3.0-flash-REAP176-46B-A5B-GGUF

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

Paper • 2510.13999 • Published Oct 15, 2025 • 22