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Cataloged from unsloth/DeepSeek-V4-Flash-Vision-Exp-GGUF
All models by unsloth →Unsloth Dynamic 3.0 achieves superior accuracy & outperforms other leading quants.
Image input requires llama.cpp b10766 or later, which is the first release containing the DeepSeek-V4 vision support from #28133 and #28154.
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.
Notes:
max reasoning effort level with temperature = 1.0, top_p = 0.95.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/ 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.
See encoding/README.md [blocked]. Both OpenAI-style JSON content
blocks and the compact <image>path</image> TXT notation are supported. The two
examples under inference/examples/ encode to identical prompts and token IDs.
See inference/README.md [blocked] for dependency installation,
checkpoint conversion, and TXT/JSON inference commands.
This repository is licensed under the MIT License [blocked].
.├── 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