.. _models_llm_deepseek-v4-pro: ======================================== DeepSeek-V4-Pro ======================================== - **Context Length:** 163840 - **Model Name:** DeepSeek-V4-Pro - **Languages:** en, zh - **Abilities:** chat, reasoning, hybrid, tools - **Description:** We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models — DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated) — both supporting a context length of one million tokens. Specifications ^^^^^^^^^^^^^^ Model Spec 1 (pytorch, 1600 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** pytorch - **Model Size (in billions):** 1600 - **Quantizations:** none - **Engines**: Transformers - **Model ID:** deepseek-ai/DeepSeek-V4-Pro - **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name DeepSeek-V4-Pro --size-in-billions 1600 --model-format pytorch --quantization ${quantization} Model Spec 2 (mlx, 1600 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** mlx - **Model Size (in billions):** 1600 - **Quantizations:** 4bit, 8bit, bf16 - **Engines**: MLX - **Model ID:** mlx-community/DeepSeek-V4-Pro-{quantization} - **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name DeepSeek-V4-Pro --size-in-billions 1600 --model-format mlx --quantization ${quantization}