Instructions to use nivvis/Step-3.5-Flash-REAP-128B-A11B-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 nivvis/Step-3.5-Flash-REAP-128B-A11B-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 nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nivvis/Step-3.5-Flash-REAP-128B-A11B-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 nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nivvis/Step-3.5-Flash-REAP-128B-A11B-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 nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nivvis/Step-3.5-Flash-REAP-128B-A11B-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 nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M

Ollama
How to use nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF with Ollama:
```
ollama run hf.co/nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nivvis/Step-3.5-Flash-REAP-128B-A11B-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": "nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF with Docker Model Runner:
```
docker model run hf.co/nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M
```

Lemonade

How to use nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Step-3.5-Flash-REAP-128B-A11B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nivvis/Step-3.5-Flash-REAP-128B-A11B-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 nivvis/Step-3.5-Flash-REAP-128B-A11B-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 nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nivvis/Step-3.5-Flash-REAP-128B-A11B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nivvis/Step-3.5-Flash-REAP-128B-A11B-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 "nivvis/Step-3.5-Flash-REAP-128B-A11B-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"

Keep on Pruning

by lazyDataScientist - opened Feb 10

Discussion

lazyDataScientist

Feb 10

Can we get this bad boy down to 40B, please 🙏

nivvis

Owner Feb 11

•

edited Feb 11

lol i hear you. 40B params? or 40GB? some details for anyone curious ..

I don't think REAP goes much further than 40% cut .. that's already very aggressive and what REAP author did (lkevincc0 -- not me)

40GB is probably feasible with ik llama quants .. (2 or 3bit?) but 40% REAP takes 5-15% quality .. Q4 normally takes another 5-10% prob worse with REAP. going past that .. 🤷 . I actually haven't tried this q4 gguf yet but should work ok.

I don't think 40B params is even close to feasible. would be fun to try with some RL healing tho but I don't have a good DPO setup.

lazyDataScientist

Feb 11

40B parameters, lol

I have seen a team drop the Qwen 3 Coder Next 80B down to a 20B model (60% pruned), it is pretty smart or a pruned and then quantized model.

nivvis

Owner Feb 11

Not saying it wouldn't be usable -- imo at that big of a drop would use some RL to be useful (IMO)

but 40B + baby DPO would probably get a pretty great flash model. point me at a fresh DPO dataset.👀

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