","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"eos_token":{"__type":"AddedToken","content":"<｜end▁of▁sentence｜>","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"pad_token":{"__type":"AddedToken","content":"<｜end▁of▁sentence｜>","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"unk_token":null}},"createdAt":"2026-10-17T02:40:38.000Z","discussionsDisabled":false,"discussionsSorting":"recently-created","downloads":0,"downloadsAllTime":0,"id":"deepseek-ai/DeepSeek-V4.1-Flash-1017","isLikedByUser":false,"availableInferenceProviders":[],"showHuggingChatEntry":false,"inference":"","lastModified":"2026-10-17T05:46:55.000Z","likes":913,"pipeline_tag":"image-text-to-text","library_name":"transformers","librariesOther":[],"trackDownloads":true,"model-index":null,"private":false,"repoType":"model","gated":false,"tags":["transformers","safetensors","deepseek_v41","text-generation","image-text-to-text","license:mit","endpoints_compatible","8-bit","fp8","region:us"],"tag_objs":[{"id":"image-text-to-text","label":"Image-Text-to-Text","type":"pipeline_tag","subType":"multimodal"},{"id":"transformers","label":"Transformers","type":"library"},{"id":"safetensors","label":"Safetensors","type":"library"},{"id":"deepseek_v41","label":"deepseek_v41","type":"other","clickable":true},{"id":"text-generation","label":"text-generation","type":"other","clickable":true},{"id":"endpoints_compatible","label":"Inference Endpoints","type":"other","clickable":true},{"id":"8-bit","label":"8-bit precision","type":"other","clickable":true},{"id":"fp8","label":"fp8","type":"other","clickable":true},{"id":"license:mit","label":"mit","type":"license"},{"type":"region","label":"🇺🇸 Region: US","id":"region:us"}],"transformersInfo":{"auto_model":"AutoModelForCausalLM","pipeline_tag":"text-generation"},"widgetData":[{"text":"Hi, what can you help me with?"},{"text":"What is 84 * 3 / 2?"},{"text":"Tell me an interesting fact about the universe!"},{"text":"Explain quantum computing in simple terms."}],"safetensors":{"parameters":{"BF16":1976441856,"F32":42307282,"F8_E4M3":204015223296,"I8":557171343360},"total":763205315794,"sharded":true,"totalFileSize":510304178606},"hasBlockedOids":false,"region":"us","isQuantized":false,"licenseFilePath":"LICENSE"},"discussionsStats":{"closed":0,"open":13,"total":13},"query":{},"inferenceContextData":{"billableEntities":[],"entityName2Providers":{}},"hasQuantizations":true,"copyToBucketNamespaces":[]}>

Libraries

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 from pip and serve model

# 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 from pip and serve model

# 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.

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},
}

Contact

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