Instructions to use apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 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 apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128
# Run inference directly in the terminal:
llama cli -hf apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128
# Run inference directly in the terminal:
llama cli -hf apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

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 apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128
# Run inference directly in the terminal:
./llama-cli -hf apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

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 apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128
# Run inference directly in the terminal:
./build/bin/llama-cli -hf apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

Use Docker

docker model run hf.co/apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

LM Studio
Jan

vLLM

How to use apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

Ollama
How to use apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 with Ollama:
```
ollama run hf.co/apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128
```

Unsloth Studio

How to use apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 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 apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 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 apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 to start chatting

How to use apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

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": "apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

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 "apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128" \
  --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 apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 with Docker Model Runner:
```
docker model run hf.co/apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128
```

Lemonade

How to use apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

Run and chat with the model

lemonade run user.DeepSeek-V4-Flash-0731-DS4-Quality128-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

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 apetersson/DeepSeek-V4-Flash-0731-DS4-Quality128

Run Hermes

hermes

Atomic Chat

apetersson commited on 1 day ago

Commit

f07f3e1

verified ·

1 Parent(s): a40bb77

docs: point ds4 requirement to main

Browse files

Replace the obsolete ds4f-mxfp4 branch reference with a recent ds4 main-branch requirement. Preserve all other remote model-card content.

Files changed (1) hide show

README.md +12 -13

README.md CHANGED Viewed

@@ -26,10 +26,10 @@ tags:
 > **Clean official weights, exact MXFP4 experts, maximum resident quality.**
-> **Required DS4 branch:** this model is not compatible with an older or stock
-> DS4 build. Its native MXFP4 tensors require the official
-> [`antirez/ds4` `ds4f-mxfp4` branch](https://github.com/antirez/ds4/tree/ds4f-mxfp4).
-> That branch runs both the target model and the supplied DSpark support model.
 > **Validation status:** conversion and CPU structural validation are complete.
 > Metal generation, DSpark acceptance, throughput, peak-memory and actual
@@ -158,19 +158,18 @@ was used only as a bounded metadata, tokenizer, tensor-order and shape template:
 ## Runtime requirement
-The required upstream runtime line is the official
-[`antirez/ds4` `ds4f-mxfp4` branch](https://github.com/antirez/ds4/tree/ds4f-mxfp4),
-not Antirez's default branch and not an older DS4 release. This is required
-because the main GGUF contains 30 native MXFP4 routed-expert tensors. A runtime
-without that branch's MXFP4 loader and Metal kernels is not compatible, even if
-it can parse the GGUF header.
-The upstream branch revision used as the base for this build was
-`4893e0c40fba03dbc85555faeb035799aa04e0b6`. The required runtime checkout is:
 ```bash
-git clone --branch ds4f-mxfp4 https://github.com/antirez/ds4.git
 cd ds4
 make
 ```

 > **Clean official weights, exact MXFP4 experts, maximum resident quality.**
+> **Required DS4 version:** this model is not compatible with an older DS4
+> build. Its native MXFP4 tensors require
+> [a recent ds4 version from the main branch](https://github.com/antirez/ds4).
+> That version runs both the target model and the supplied DSpark support model.
 > **Validation status:** conversion and CPU structural validation are complete.
 > Metal generation, DSpark acceptance, throughput, peak-memory and actual
 ## Runtime requirement
+Use [a recent ds4 version from the main branch](https://github.com/antirez/ds4).
+This is required because the main GGUF contains 30 native MXFP4 routed-expert
+tensors. An older runtime without the MXFP4 loader and Metal kernels now in
+`main` is not compatible, even if it can parse the GGUF header.
+Install or update a `main`-branch checkout:
 ```bash
+git clone https://github.com/antirez/ds4.git
 cd ds4
+git switch main
+git pull --ff-only
 make
 ```