","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"eos_token":{"__type":"AddedToken","content":"","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"pad_token":{"__type":"AddedToken","content":"","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 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 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=" \ --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.