Instructions to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF",
	filename="DeepSeek-V4-Flash-REAP-K128.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-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 sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF
# Run inference directly in the terminal:
llama cli -hf sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF
# Run inference directly in the terminal:
llama cli -hf sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

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 sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF
# Run inference directly in the terminal:
./llama-cli -hf sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

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 sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

Use Docker

docker model run hf.co/sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

LM Studio
Jan

vLLM

How to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-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": "sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

Ollama
How to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF with Ollama:
```
ollama run hf.co/sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF
```

Unsloth Studio

How to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF to start chatting

How to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-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 sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

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 sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

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 "sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF" \
  --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"

Docker Model Runner
How to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF with Docker Model Runner:
```
docker model run hf.co/sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF
```

Lemonade

How to use sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

Run and chat with the model

lemonade run user.deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

DeepSeek V4 Flash — REAP K128 (Mixed Precision)

REAP-pruned DeepSeek V4 Flash at K128 (128 routed experts per MoE layer). Prunes 50% of routed experts via Cerebras REAP (Router-weighted Expert Activation Pruning), preserving all attention, embeddings, shared experts, router, and MTP components.

At a Glance


Base model	DeepSeek V4 Flash
Donor GGUF	antirez Layers37-42Q4KExperts (90.9 GiB)
Pruning method	REAP (Cerebras Research)
Routed experts	128 per layer (down from 256)
Kept slots	5,888 / 11,008
Hash-preserved	Layers 0-2 (256 experts each)
Pruned	Layers 3-42 (128 experts each)
Format	ds4-compact-v1 GGUF
File size	52.04 GiB
Mixed precision	Layers 3-36: Q2_K/IQ2_XXS · Layers 37-42: Q4_K

Note: This variant preserves the donor's mixed quantization (Q4_K in layers 37-42, IQ2_XXS/Q2_K elsewhere). Requires a ds4 runtime that handles per-layer quant type dispatch. If you encounter "expected IQ2_XXS expert tensors" errors, use the uniform variant.

Domain Split (Calibration)

8,000 prompts · 5.0M tokens · 1.3B routed expert observations

Domain	Share
Coding & development	35-40%
Agentic tool-calling	16%
Research & knowledge	15-20%
Math & science	10-15%
Design & planning	5-10%
Trivia & general QA	3-5%

Calibration used the REAP activation_energy_sum2 score metric with 4,096 token context per prompt. Top-to-bottom expert score gap in layer 3: 2,200x (strong pruning signal).

How to Run

Requires eouya2/ds4-for-reaped (ds4 engine with compact GGUF support):

git clone https://github.com/eouya2/ds4-for-reaped
cd ds4-for-reaped
make cuda-spark -j$(nproc)  # DGX Spark / CUDA
# or: make                    # Metal / macOS

./ds4 --cuda -m DeepSeek-V4-Flash-REAP-K128.gguf --ctx 131072

API server mode:

./ds4-server --cuda -m DeepSeek-V4-Flash-REAP-K128.gguf \
  --host 0.0.0.0 --port 17777 --ctx 131072

How It Was Built

Donor GGUF: Downloaded antirez Layers37-42Q4KExperts variant (90.9 GiB) — Q4_K experts in deep layers, IQ2_XXS/Q2_K elsewhere
Calibration: 8,000 prompts collected and run through ds4's imatrix collector on a DGX Spark (NVIDIA GB10) at 4,096 token context
REAP scoring: Imatrix activation data converted to per-expert REAP scores using activation_energy_sum2
Pruning: 50% expert removal via ds4_prune_gguf.py from eouya2/reap-for-ds4. Layers 0-2 (hash-routed) preserved. Expert tensors copied byte-for-byte — no dequant/requant.
Output: ds4-compact-v1 GGUF

No fine-tuning. Purely structural expert removal. Weights are unmodified — a subset of the original experts.

Acknowledgments

DeepSeek — DeepSeek V4 Flash base model
antirez — ds4 engine and GGUF quants
eouya2 — ds4-for-reaped and reap-for-ds4
Cerebras Research — REAP (code)
NVIDIA — DGX Spark hardware

Downloads last month: 932

GGUF

Model size

155B params

Architecture

deepseek4

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Paper for sleepyeldrazi/deepseek-v4-flash-reap-k128-Q2-Q4-Mixed-GGUF

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

Paper • 2510.13999 • Published Oct 15, 2025 • 20