Instructions to use AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AMAImedia/DeepSeek-V4.1-Flash-FP8-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 AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

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 AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

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 AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

Use Docker

docker model run hf.co/AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMAImedia/DeepSeek-V4.1-Flash-FP8-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": "AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

SGLang

How to use AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF with Ollama:
```
ollama run hf.co/AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K
```
Unsloth Desktop

How to use AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

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": "AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF with Docker Model Runner:
```
docker model run hf.co/AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K
```

Lemonade

How to use AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

Run and chat with the model

lemonade run user.DeepSeek-V4.1-Flash-FP8-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use AMAImedia/DeepSeek-V4.1-Flash-FP8-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 AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

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 AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K

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 "AMAImedia/DeepSeek-V4.1-Flash-FP8-GGUF:Q2_K" \
  --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"

DeepSeek-V4.1-Flash-FP8-GGUF / evaluation /README.md

AMAImedia

Duplicate from deepseek-ai/DeepSeek-V4.1-Flash

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Running DeepSWE with `dsh-minimal` and `mini-swe-agent`

1. Prerequisites

Docker, running and able to pull images
Python 3.12+ and uv
An endpoint and key for any DeepSeek-API-compatible service (the DeepSeek official API is used as the example below)

export DEEPSEEK_API_KEY=sk-your-key-here
export DEEPSEEK_BASE_URL=https://api.deepseek.com

2. Get Pier and DeepSWE

git clone https://github.com/datacurve-ai/pier.git
git -C pier checkout 0c802fc067a425345b24d1c69411aa98acf61a1d

git clone https://github.com/datacurve-ai/deep-swe.git
git -C deep-swe checkout 0b9fabbb63b9104d678fe965e1632f2dd9eaa2ea

3. Patch and install Pier

dsh-minimal.patch ships next to this document. Treat it as a reference patch and adapt it to your own setup.

cd pier
git apply /path/to/dsh-minimal.patch
uv sync

What the patch changes:

Adds the dsh-minimal agent, which drives the Harness SDK and folds its event stream into a Pier ATIF trajectory. The SDK artifact is never installed into the image: step 4's --mounts-json bind-mounts it read-only into the sandbox, so no trial installs anything.
Appends a runtime-constraints section to the task instruction for both agents: work in /app, leave /tests alone, no network or package mirror.
Passes test-runner concurrency caps into the container: Docker's --cpus is only a quota, so nproc inside the container reports the host's core count and test runners size their worker pools from that rather than from the container's share.
Enables IPv6 loopback in the container: Docker disables it by default, so suites that bind ::1 are skipped and scored as failures.
Makes --mounts-json additive instead of replacing the default mounts, keeping the /logs binds that carry agent logs and collected patches.

4. Run the suite

Both agents take the same task set, concurrency, and --no-delete (which keeps the task images cached between trials). Repeat each run with a different --job-name and average the results.

Each trial's container takes the 2 CPUs and 8 GB its task declares, so size -n against the host's cores and memory.

`mini-swe-agent`

Pier installs it into each task image at trial time, so no host-side preparation is needed.

uv run pier run \
  -p ../deep-swe/tasks \
  --agent mini-swe-agent \
  --model deepseek/deepseek-flash \
  --ak reasoning_effort=max \
  --ak cost_limit=0 \
  --ae DEEPSEEK_API_KEY="$DEEPSEEK_API_KEY" \
  --ae DEEPSEEK_BASE_URL="$DEEPSEEK_BASE_URL" \
  -n 32 --no-delete -r 2 --job-name deepswe-mini-run1 -y

--model takes a litellm-style provider/model string.

`dsh-minimal`

Install the Harness SDK artifact once on the host, then bind-mount it read-only into every container.

mkdir -p ~/dsh-minimal && cd ~/dsh-minimal
uv pip install --target dsh-dist \
  --python-version 3.12 --python-platform x86_64-manylinux_2_28 \
  'deepseek-harness-sdk==0.1.5.*'

uv run pier run \
  -p ../deep-swe/tasks \
  --agent dsh-minimal \
  --model deepseek-flash \
  --ak reasoning_effort=max \
  --ae DEEPSEEK_API_KEY="$DEEPSEEK_API_KEY" \
  --ae DEEPSEEK_BASE_URL="$DEEPSEEK_BASE_URL" \
  --mounts-json '[{"type":"bind","source":"'"$HOME"'/dsh-minimal/dsh-dist","target":"/opt/dsh-minimal","read_only":true}]' \
  -n 32 --no-delete --job-name deepswe-dsh-run1 -y

In --mounts-json, source is the absolute path of the dsh-dist directory above; target is always /opt/dsh-minimal.

5. Read the results

jobs/<job-name>/
  result.json              pass rate and token totals
  <task>__<id>/
    result.json            reward, fail-to-pass / pass-to-pass counts, tokens
    agent/trajectory.json  full ATIF trajectory (dsh-minimal)
    agent/mini-swe-agent.trajectory.json   mini-swe-agent trajectory
    verifier/              reward.json and test output

Browse a job with uv run pier view jobs/<job-name>.

Running DeepSWE with dsh-minimal and mini-swe-agent

1. Prerequisites

2. Get Pier and DeepSWE

3. Patch and install Pier

4. Run the suite

mini-swe-agent

dsh-minimal

5. Read the results

Running DeepSWE with `dsh-minimal` and `mini-swe-agent`

`mini-swe-agent`

`dsh-minimal`