--- tags: - unsloth base_model: - deepseek-ai/DeepSeek-V4-Flash-Vision-Exp license: mit library_name: transformers ---

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

# 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: 1. 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`. 2. † 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. ```text . ├── 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`](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`](inference/README.md) for dependency installation, checkpoint conversion, and TXT/JSON inference commands. ## License This repository is licensed under the [MIT License](LICENSE).