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Image-Text-to-Text

Transformers

Safetensors

deepseek_v41

text-generation

8-bit precision

fp8

License: mit

Model card Files Files and versions xet

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Use this model Instructions to use deepseek-ai/DeepSeek-V4.1-Flash-1017 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers How to use deepseek-ai/DeepSeek-V4.1-Flash-1017 with Transformers:

Use a pipeline as a high-level helper

from transformers import pipeline

pipe = pipeline("image-text-to-text", model="deepseek-ai/DeepSeek-V4.1-Flash-1017")

pip install -U transformers accelerate

Load model directly

from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V4.1-Flash-1017", device_map="auto")

Notebooks

Google Colab

Kaggle

Local Apps Settings

vLLM How to use deepseek-ai/DeepSeek-V4.1-Flash-1017 with vLLM:

Install vLLM from pip:

pip install vllm

Start the vLLM server:

vllm serve "deepseek-ai/DeepSeek-V4.1-Flash-1017"

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:8000/v1/completions"
-H "Content-Type: application/json"
--data '{ "model": "deepseek-ai/DeepSeek-V4.1-Flash-1017", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-V4.1-Flash-1017

SGLang How to use deepseek-ai/DeepSeek-V4.1-Flash-1017 with SGLang:

Install SGLang from pip:

pip install sglang

Start the SGLang server:

python3 -m sglang.launch_server
--model-path "deepseek-ai/DeepSeek-V4.1-Flash-1017"
--host 0.0.0.0
--port 30000

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:30000/v1/completions"
-H "Content-Type: application/json"
--data '{ "model": "deepseek-ai/DeepSeek-V4.1-Flash-1017", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'

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 "deepseek-ai/DeepSeek-V4.1-Flash-1017"
--host 0.0.0.0
--port 30000

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:30000/v1/completions"
-H "Content-Type: application/json"
--data '{ "model": "deepseek-ai/DeepSeek-V4.1-Flash-1017", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'

Docker Model Runner How to use deepseek-ai/DeepSeek-V4.1-Flash-1017 with Docker Model Runner:

docker model run hf.co/deepseek-ai/DeepSeek-V4.1-Flash-1017

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DeepSeek-V4.1-Flash-1017 Introduction

Prompt Encoding

Minimal Inference

License

Citation

Contact

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.

† 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.

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}, }

Contact

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

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Model size

763B params

Tensor type

BF16 ·

F32 ·

F8_E4M3 ·

I8 ·

Files info