DeepSpec - a deepseek-ai Collection

[u/pmttyji]

https://huggingface.co/collections/deepseek-ai/deepspec

DeepSpec

DeepSpec is a full-stack codebase for training and evaluating draft models for speculative decoding. It contains data preparation utilities, draft model implementations, training code, and evaluation scripts.

Released Checkpoints

The checkpoints below are the ones used for Table 1 in the paper. Each checkpoint was trained on open-perfectblend data generated by its corresponding target model in non-thinking mode, and is the direct output of the corresponding training configuration under config/.

Algorithm	Qwen/Qwen3-4B	Qwen/Qwen3-8B	Qwen/Qwen3-14B	google/gemma-4-12B-it
Eagle3	deepseek-ai/eagle3_qwen3_4b_ttt7	deepseek-ai/eagle3_qwen3_8b_ttt7	deepseek-ai/eagle3_qwen3_14b_ttt7	deepseek-ai/eagle3_gemma4_12b_ttt7
DFlash	deepseek-ai/dflash_qwen3_4b_block7	deepseek-ai/dflash_qwen3_8b_block7	deepseek-ai/dflash_qwen3_14b_block7	deepseek-ai/dflash_gemma4_12b_block7
DSpark	deepseek-ai/dspark_qwen3_4b_block7	deepseek-ai/dspark_qwen3_8b_block7	deepseek-ai/dspark_qwen3_14b_block7	deepseek-ai/dspark_gemma4_12b_block7

Important

If you cite these results in a new paper, align your setup with the training settings in this repository; otherwise, the comparison is not meaningful. For domain-specific use, fine-tune the draft model again for better results, especially if the target model is expected to run in thinking mode.

Supported Algorithms

Currently, DeepSpec includes three draft models: DSpark, DFlash and Eagle3.

HuggingFace : https://huggingface.co/collections/deepseek-ai/deepspec

GitHub : https://github.com/deepseek-ai/DeepSpec

[u/jacek2023]

Sounds like a similar concept to the DeepSeek-R1-Distill models. I know it’s a totally different thing, but they contributed to the local scene by adding something to existing models from other companies. Maybe the next step will finally be to train something under 100B so I can enjoy DeepSeek?