DeepSeek-Pro-0813

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What DeepSeek-Pro-0813 is

DeepSeek-V4-Pro-0813 Introduction DeepSeek-V4-Pro-0813 is the official release of DeepSeek-V4-Pro, superseding the preview version, with greatly enhanced agentic capabilities and performance improvements that are especially pronounced in production environments. It is built on the DeepSeek-V4-Pro (Preview) model structure, with a DSpark speculative decoding module attached. DeepSeek-V4-Pro-0813 outperforms DeepSeek-V4-Pro (Preview) on the benchmarks listed below, and is broadly competitive with the strongest proprietary models available. Notes: 1. For the code-agent tasks among the public benchmarks above, DeepSeek-V4-Pro-0813 is evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the max reasoning effort level with temperature = 1.0, topp = 0.95. 2. † DSBench-FullStack is an internal full-stack development test set; DSBench-Hard is an internal test set of difficult coding-agent problems. Chat Template This release does not include a Jinja-format chat template. Instead, we provide a dedicated encoding folder with Python scripts and test cases demonstrating how to encode messages in OpenAI-compatible format into input strings for the model, and how to parse the model's text output. Please refer to the encoding folder for full documentation. The reasoningeffort parameter now supports three levels — low, high, and max — which control how much deliberation the model spends before answering. A brief example: How to Run with vLLM DSpark speculative decoding is enabled with a single flag — add --speculative-config with method: dspark to your vLLM launch command: --speculative-config '{"method":"dspark","numspeculativetokens":7,"draftsamplemethod":"greedy"}' For example, the command below serves the model with vLLM on a single 4×GB300 node. See the vLLM recipe for detailed instructions and other hardware configurations. How to Run with SGLang Enable DSpark with --speculative-algorithm DSPARK and do not set a separate --speculative-draft-model-path as the target and draft weights therefore come from the same checkpoint. See the SGLang cookbook for detailed instructions, benchmarks and other hardwares configurations. How to Run Locally Please

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

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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.

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Call it like any OpenAI endpoint

Once AxForge deploys unsloth-deepseek-pro-0813 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (unsloth-deepseek-pro-0813 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":"unsloth-deepseek-pro-0813","messages":[{"role":"user","content":"Hello"}]}'

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