DeepSeek-Exp

About

What DeepSeek-Exp is

DeepSeek-V3.2-Exp Introduction We are excited to announce the official release of DeepSeek-V3.2-Exp, an experimental version of our model. As an intermediate step toward our next-generation architecture, V3.2-Exp builds upon V3.1-Terminus by introducing DeepSeek Sparse Attention—a sparse attention mechanism designed to explore and validate optimizations for training and inference efficiency in long-context scenarios. This experimental release represents our ongoing research into more efficient transformer architectures, particularly focusing on improving computational efficiency when processing extended text sequences. - DeepSeek Sparse Attention (DSA) achieves fine-grained sparse attention for the first time, delivering substantial improvements in long-context training and inference efficiency while maintaining virtually identical model output quality. - To rigorously evaluate the impact of introducing sparse attention, we deliberately aligned the training configurations of DeepSeek-V3.2-Exp with V3.1-Terminus. Across public benchmarks in various domains, DeepSeek-V3.2-Exp demonstrates performance on par with V3.1-Terminus. Update - 2025.11.17: We have identified that previous versions of the inference demo code contained an implementation discrepancy in Rotary Position Embedding (RoPE) within the indexer module, potentially leading to degraded model performance. Specifically, the input tensor to RoPE in the indexer module requires a non-interleaved layout, whereas RoPE in the MLA module expects an interleaved layout. This issue has now been resolved. Please refer to the updated version of the inference demo code and take note of this implementation detail. How to Run Locally HuggingFace We provide an updated inference demo code in the inference folder to help the community quickly get started with our model and understand its architectural details. First convert huggingface model weights to the the format required by our inference demo. Set MP to match your available GPU count: Launch the interactive chat interface and start exploring DeepSeek's capabilities: SGLang Installation with Docker Launch Command vLLM vLLM provides day-0 support of DeepSeek-V3.2-Exp.

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys deepseek-exp for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (deepseek-exp below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-exp","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Licence

Open weights

Sources

Weights & code

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