Instructions to use andreaborio/DeepSeek-V4-Flash-Hebrus-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 andreaborio/DeepSeek-V4-Flash-Hebrus-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 andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF
# Run inference directly in the terminal:
llama cli -hf andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF
# Run inference directly in the terminal:
llama cli -hf andreaborio/DeepSeek-V4-Flash-Hebrus-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 andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF
# Run inference directly in the terminal:
./llama-cli -hf andreaborio/DeepSeek-V4-Flash-Hebrus-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 andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF

Use Docker

docker model run hf.co/andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF

LM Studio
Jan

vLLM

How to use andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andreaborio/DeepSeek-V4-Flash-Hebrus-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": "andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF

Ollama
How to use andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF with Ollama:
```
ollama run hf.co/andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF
```

Unsloth Studio

How to use andreaborio/DeepSeek-V4-Flash-Hebrus-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 andreaborio/DeepSeek-V4-Flash-Hebrus-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 andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF to start chatting

How to use andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf andreaborio/DeepSeek-V4-Flash-Hebrus-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": "andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use andreaborio/DeepSeek-V4-Flash-Hebrus-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 andreaborio/DeepSeek-V4-Flash-Hebrus-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 andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf andreaborio/DeepSeek-V4-Flash-Hebrus-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 "andreaborio/DeepSeek-V4-Flash-Hebrus-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 andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF with Docker Model Runner:
```
docker model run hf.co/andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF
```

Lemonade

How to use andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF

Run and chat with the model

lemonade run user.DeepSeek-V4-Flash-Hebrus-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hebrus Studio — Metal first. SSD when memory ends.

DeepSeek V4 Flash — Hebrus GGUF releases

This repository is the single release container for the versioned DeepSeek V4 Flash artifacts used by the Hebrus inference engine. Hebrus began as a fork of Salvatore Sanfilippo's antirez/ds4. Historical DS4 names remain only in filenames and serialized compatibility fields. The current runtime artifact embeds its routed MoE weights once as a self-describing ds4.expert_major.v2 store. It needs no sidecar, runtime repack, or second routed-weight copy.

Compatibility: existing filenames, the dsbox.json manifest, and the ds4.expert_major.v2 marker remain unchanged serialized contracts.

Versions

File	Status	Runtime
`DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2-imatrix-DS4-ExpertMajor-v2.gguf`	Current	Hebrus `main` at `57acfd4` or newer
`DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2-imatrix.gguf`	Legacy canonical source and conversion input	Generic GGUF tooling; rejected by current Hebrus inference

Hebrus Studio and the root dsbox.json select only ExpertMajor v2. The canonical file is retained for provenance, checksum-stable conversion, and generic tooling; it is not a fallback for current Hebrus. ExpertMajor v1, sidecars, canonical MoE inference, and non-Metal inference are intentionally unsupported.

The current file uses Hebrus' ExpertMajor v2 extension rather than a portable GGUF layout. Generic GGUF runtimes cannot reconstruct or execute its opaque routed store.

Current artifact

Property	Value
Base model	`deepseek-ai/DeepSeek-V4-Flash`
File	`DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2-imatrix-DS4-ExpertMajor-v2.gguf`
Size	86,720,114,272 bytes (80.76 GiB)
SHA-256	`8378080263eb9224f7228d72e2afa4ac3cf74a116023fdec2c596ff228a33e3f`
Container	GGUF v3 with one embedded `ds4.expert_major.v2` store
Routed geometry	43 routed layers, 256 experts per layer, top-6
Expert record	gate + up + down, 7,077,888 contiguous bytes
Routed payload	77,913,391,104 bytes
Quantization	IQ2_XXS routed gate/up, Q2_K routed down, selected Q8_0/F16/F32 dense tensors
Qualified hardware	Apple Silicon Metal, at least 64 GiB unified memory

The ExpertMajor v2 conversion changes only routed-weight storage order and the GGUF tensor directory. It does not requantize model values. Non-routed tensors are copied byte-for-byte from the canonical source and the routed payload is stored once.

The retained canonical source is 86,720,111,488 bytes with SHA-256 efc7ed607ff27076e3e501fc3fefefa33c0ed8cf1eff483a2b7fdc0c2e616668. It was mirrored checksum-for-checksum from antirez/deepseek-v4-gguf revision 9170bf42beb77f38006e016503ecace31f2bd9a0. Quantization credit belongs to antirez and the DS4 project.

Run with Hebrus

Use andreaborio/hebrus main at commit 57acfd408a3154851a0c59be432904300abb3b6c or a compatible descendant:

git clone https://github.com/andreaborio/hebrus.git
cd hebrus
make -j

./hebrus \
  -m /absolute/path/to/DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2-imatrix-DS4-ExpertMajor-v2.gguf \
  --ctx 8192

The ExpertMajor v2 store, Metal backend, and memory policy are selected automatically. No Metal, SSD-streaming, power, sidecar, pack, or format flag is needed. The qualified minimum is 64 GiB unified memory; 128 GiB is the more comfortable tier for this 80.76 GiB artifact.

Verification

The deterministic converter verifies source identity, GGUF metadata, non-routed tensors, every routed expert component, alignment, embedded manifest, and the complete ExpertMajor payload. Canonical and ExpertMajor v2 runs produced identical frontier-logit hashes in the retained 128- and 768-token correctness gates. The 768-token prefill comparison measured 95.82 versus 95.74 tokens/s, so the layout is a locality/runtime contract rather than a claim of standalone universal acceleration.

The clean v2-only refactor gate on an M5 Pro with 64 GiB measured 20.93/13.20 tokens/s prefill/decode from a colder file cache and 23.78/13.95 tokens/s on the immediate warm repeat. Both runs produced frontier-logit SHA-256 71fd3be0732e0fe97b9f104112911dc937896257c604c64eae851e36fa142441 with zero swapout.

The release runtime uses a pressure-gated cache phase: 259 expert records for large prefill, 4,129 through 32K decode, and 2,065 at 65K and above. On the same 64 GiB M5 Pro, the zero-swap prose lanes measured:

Context	Prefill	Decode	TPOT p50/p95
32K+128	164.43 t/s	7.27 t/s	105.410/149.943 ms
65K+128	137.29 t/s	6.59 t/s	n/a
100K+128	145.11 t/s	6.44 t/s	n/a

The original 32K AUTO controls can swap, so no 32K speedup percentage is published. The promoted result is the safer adaptive policy, exact output, and bounded long-tail latency under the qualified workload.

Limitations

Apple Silicon Metal is the only qualified inference backend.
At least 64 GiB unified memory is required.
Current Hebrus deliberately rejects the retained canonical file, ExpertMajor v1, sidecars, CPU, CUDA, ROCm, and distributed inference for this model.
Performance depends on storage state, memory pressure, context length, routing locality, and thermals.

License and attribution

The source repository declares MIT. DeepSeek retains the base-model copyright; the GGUF is redistributed under the base model's release terms. Quantization credit belongs to antirez and the DS4 project.

Downloads last month: 808

GGUF

Model size

85B params

Architecture

deepseek4

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for andreaborio/DeepSeek-V4-Flash-Hebrus-GGUF

Base model

deepseek-ai/DeepSeek-V4-Flash

Quantized

(117)

this model