AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
","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":"2026-08-31T06:16:18.000Z","discussionsDisabled":false,"discussionsSorting":"recently-created","downloads":0,"downloadsAllTime":0,"id":"deepseek-ai/DeepSeek-V4-Flash-Vision-Exp","isLikedByUser":false,"availableInferenceProviders":[],"showHuggingChatEntry":false,"inference":"","lastModified":"2026-08-31T09:20:30.000Z","likes":127,"pipeline_tag":"text-generation","library_name":"transformers","librariesOther":[],"trackDownloads":true,"model-index":null,"private":false,"repoType":"model","gated":false,"tags":["transformers","safetensors","deepseek_v4","text-generation","license:mit","endpoints_compatible","8-bit","fp8","region:us"],"tag_objs":[{"id":"text-generation","label":"Text Generation","type":"pipeline_tag","subType":"nlp"},{"id":"transformers","label":"Transformers","type":"library"},{"id":"safetensors","label":"Safetensors","type":"library"},{"id":"deepseek_v4","label":"deepseek_v4","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","processor":"AutoTokenizer"},"widgetData":[{"text":"My name is Julien and I like to"},{"text":"I like traveling by train because"},{"text":"Paris is an amazing place to visit,"},{"text":"Once upon a time,"}],"safetensors":{"parameters":{"BF16":1949960576,"F32":37754174,"F8_E4M3":6304038912,"I8":296352743424,"I64":2327040},"total":304646824126,"sharded":true,"totalFileSize":167825030229},"hasBlockedOids":false,"region":"us","isQuantized":false,"licenseFilePath":"LICENSE"},"discussionsStats":{"closed":1,"open":3,"total":4},"query":{},"inferenceContextData":{"billableEntities":[],"entityName2Providers":{}},"hasQuantizations":true,"copyToBucketNamespaces":[]}"> DeepSeek-V4-Flash-Vision-Exp Introduction We are excited to introduce DeepSeek-V4-Flash-Vision-Exp, our first experimental multimodal model in the DeepSeek-V4 family. It builds on the DeepSeek-V4-Flash architecture by incorporating visual modules and undergoing continued training to unlock visual understanding capabilities. Compared to DeepSeek-V4-Flash-0731, DeepSeek-V4-Flash-Vision-Exp achieves substantial improvements on its multimodal agent capabilities, while maintaining comparable performance on text-only agent tasks. Benchmark DeepSeek-V4-Flash-Vision-Exp DeepSeek-V4-Flash-0731 Opus-4.8 Text Agent Capabilities Terminal Bench 2.1 83.9 82.7 85.0 NL2Repo 57.7 54.2 69.7 Cybergym 75.3 76.7 78.3 DeepSWE 59.3 54.4 58.0 Toolathlon-Verified 75.9 70.3 76.2 DSBench-Hard 63.6 59.6 71.7 AutomationBench (Public) 25.7 25.1 27.2 Multimodal Agent Capabilities ApexBench (Pass@1) 36.5 26.2† 39.4 Agents' Last Exam 27.3 25.2† 25.7 Chartography 64.3 - 65.0 ZeroBench (Pass@5) 35.0 - 34.0 Notes: For the text agent benchmarks above, DeepSeek models are evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the max reasoning effort level with temperature = 1.0, top_p = 0.95. † For ApexBench and Agents' Last Exam, DeepSeek-V4-Flash-0731 ignores the multimodal elements in the input. Repository layout This repository contains the tokenizer, prompt encoding reference, and a minimal PyTorch inference implementation for DeepSeek-V4 Flash Vision. The reference inference covers the vision encoder and aligner, DFlash attention, MoE, Hyper-Connections, and the DSpark forward path. . ├── encoding/ # OpenAI-style messages -> model prompt ├── inference/ # weight conversion and minimal inference │ └── examples/ # equivalent TXT and JSON vision prompts ├── config.json # Hugging Face model metadata ├── generation_config.json ├── model.safetensors.index.json ├── tokenizer.json └── tokenizer_config.json encoding/ and inference/ deliberately remain separate: prompt formatting does not depend on PyTorch, while inference imports the sibling encoding module with an explicit Python path. No symlinks are required. The tokenizer files are regular files so that the repository can be uploaded to Hugging Face without relying on local filesystem symlinks. The large model shards are described by model.safetensors.index.json and are not duplicated inside the source checkout used to assemble this repository. Prompt encoding See encoding/README.md. Both OpenAI-style JSON content blocks and the compact path TXT notation are supported. The two examples under inference/examples/ encode to identical prompts and token IDs. Minimal inference See inference/README.md for dependency installation, checkpoint conversion, and TXT/JSON inference commands. License This repository is licensed under the MIT License. Downloads last month - 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