DeepSeek-V4.1-Flash-1017-FP8-GGUF

Instructions to use AMAImedia/DeepSeek-V4.1-Flash-1017-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-1017-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-1017-FP8-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AMAImedia/DeepSeek-V4.1-Flash-1017-FP8-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AMAImedia/DeepSeek-V4.1-Flash-1017-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-1017-FP8-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf AMAImedia/DeepSeek-V4.1-Flash-1017-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-1017-FP8-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf AMAImedia/DeepSeek-V4.1-Flash-1017-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-1017-FP8-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf AMAImedia/DeepSeek-V4.1-Flash-1017-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-1017-FP8-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AMAImedia/DeepSeek-V4.1-Flash-1017-FP8-GGUF:Q2_K

Use Docker

docker model run hf.co/AMAImedia/DeepSeek-V4.1-Flash-1017-FP8-GGUF:Q2_K

Llama new
LM Studio
Jan

vLLM

How to use AMAImedia/DeepSeek-V4.1-Flash-1017-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-1017-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-1017-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-1017-FP8-GGUF:Q2_K

SGLang

How to use AMAImedia/DeepSeek-V4.1-Flash-1017-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-1017-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-1017-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-1017-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-1017-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-1017-FP8-GGUF with Ollama:
```
ollama run hf.co/AMAImedia/DeepSeek-V4.1-Flash-1017-FP8-GGUF:Q2_K
```
Unsloth Desktop

How to use AMAImedia/DeepSeek-V4.1-Flash-1017-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-1017-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-1017-FP8-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AMAImedia/DeepSeek-V4.1-Flash-1017-FP8-GGUF with Docker Model Runner:
```
docker model run hf.co/AMAImedia/DeepSeek-V4.1-Flash-1017-FP8-GGUF:Q2_K
```

Lemonade

How to use AMAImedia/DeepSeek-V4.1-Flash-1017-FP8-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AMAImedia/DeepSeek-V4.1-Flash-1017-FP8-GGUF:Q2_K

Run and chat with the model

lemonade run user.DeepSeek-V4.1-Flash-1017-FP8-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use AMAImedia/DeepSeek-V4.1-Flash-1017-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-1017-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-1017-FP8-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AMAImedia/DeepSeek-V4.1-Flash-1017-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-1017-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-1017-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"

⚡ Each donation funds the next large quant.

I host free GGUF or MoE quants as independent research.
Local hardware: Mechrevo Kuangshi GM7AG0M — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro.
Good for imatrix and 0.6–35B-class work in RAM. 9B+ and searches need rented H200/Blackwell, typically $100 per quant.

🎉 Boosty🦄 | ☕ Buy Me a Coffee🦄 | ⭐ DonationAlerts🦄

💚 Thanks to Hugging Face for extra storage.🦄

NOESIS / AMAImedia

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators)

Founder: Ilia Bolotnikov
Organization: AMAImedia.com
X (Twitter): @AMAImediacom
LinkedIn: Ilia Bolotnikov
Telegram: @djbionicl
Release date: 2026-10-17

AMAImedia

Original repository: deepseek-ai/DeepSeek-V4.1-Flash-1017
Released GGUF repository: mrfakename/DeepSeek-V4.1-Flash-1017-GGUF

DeepSeek-V4.1-Flash-1017

Technical Report 👁️

Introduction

DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses.

Benchmark	DS-V4.1-Flash-1017	DS-V4.1-Flash	K3	GLM-5.3	GPT-5.6 Sol	Opus 5
HLE w/o tools	42.6 (44.0†)	36.8 (39.1†)	43.5	42.0†	44.5	56.3
Terminal-Bench 4.0	42.3	31.2	12.6	37.9	39.9	51.8
DeepSWE v1.1	77.0	74.2	67.5	66.9	73.0	74.0
ProgramBench	29.1	20.3	17.5	19.0	23.0	37.0
NL2Repo-Bench	68.2	65.4	58.0	58.0	56.8	75.3
Automation-Bench v1.0.6	60.5	54.8	46.7	48.8	45.8	50.3
Agents’ Last Exam	35.4	31.8	27.6	28.5	26.7	28.6
Apex Bench v1.1	72.1	64.6	47.8	56.6	54.3	65.8
Chartography (w/tools)	83.0	78.9	68.1	—	79.9	84.0
BabyVision (w/tools)	92.5	89.6	85.7	—	88.9	94.1
ZeroBench-main (w/tools)	51.0	49.0	41.0	—	53.0	52.0

† denotes the text-only subset of HLE. We use the Claude Code harness for Terminal-Bench 4.0 and the Mini-SWE-V2 harness for DeepSWE v1.1.

Prompt Encoding

This release does not include a Jinja-format chat template. The encoding folder contains a self-contained Python reference implementation (encoding.py) with test cases for multi-turn conversations, tool calling, thinking mode, numeric reasoning effort, mid-conversation system messages, and interleaved image content.

For production use, we additionally release deepseek-recipe, a set of Rust libraries with Python bindings that provides the same prompt format as a maintained, protocol-aware toolkit. It converts Messages, Chat Completions, and Responses API requests into the Conversation format, encodes them into DeepSeek V4 and V4.1 prompts or token IDs, and parses model output back into complete or streamed responses — covering thinking, tool calls, images, and generation settings. Model inference, tool execution, and HTTP transport are left to the caller.

Minimal Inference

Please refer to the inference folder for instructions on weight conversion and running inference locally.

Recommended sampling parameters:

Parameter	Value
`temperature`	1.0
`top_p`	1.0
`context_window`	1M tokens
`max_tokens`	≥ 256K

License

This repository and the model weights are licensed under the MIT License.

Citation

@misc{deepseekai2026deepseekv41flash,
      title={DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression},
      author={DeepSeek-AI},
      year={2026},
}

DeepSeek-V4.1-Flash-1017 GGUF

GGUF quants of deepseek-ai/DeepSeek-V4.1-Flash-1017.

Folder	Size	Files	Notes
`Q4_K_M`	414 GiB	10	The routed experts are already about 4-bit, so this saves less than usual.
`Q2_K`	246 GiB	10	Smallest usable size.

These are plain quants with no imatrix. Each folder holds a split GGUF. Point llama.cpp at the -00001-of- file and it loads the rest.

Running

Mainline llama.cpp doesn't support DeepSeek V4.1 yet. These files were made with the branch from ggml-org/llama.cpp#28696 at commit 2076412, and they need that branch or a build that includes it.

git clone https://github.com/vcruz305/llama.cpp && cd llama.cpp
git checkout 2076412598e99127ea2554467b2b7530856895d3
cmake -B build && cmake --build build -j

The two engram tables are large and read a few rows per token. Keeping them on the CPU works well, for example -ot "engram_embd.weight=CPU". The PR has a tested command line.

Text only for now. The llama.cpp branch can't convert the vision tower yet.

Acknowledgements

Huge thanks to Hugging Face for generously sponsoring the compute used for this project 🤗

x.com/realmrfakename

Contact

If you have any questions, please raise an issue or contact us at service@deepseek.com.

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