deepseek-ai/DeepSpec

Hardware: the default configs and scripts assume a single node with 8 GPUs. For fewer GPUs, reduce CUDA_VISIBLE_DEVICES.

Evaluation

bash scripts/eval/eval.sh

eval.sh runs eval.py against a trained draft checkpoint over the speculative-decoding benchmarks in eval_datasets/ (gsm8k, math500, aime25, humaneval, mbpp, livecodebench, mt-bench, alpaca, arena-hard-v2). Set:

• target_name_or_path — the target model the draft was trained against (e.g. Qwen/Qwen3-4B),
• draft_name_or_path — the draft checkpoint, e.g. ~/checkpoints/deepspec/dspark_block8_qwen3_4b/step_latest.

Supported Algorithms

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

License

DeepSpec is released under the MIT License. It includes code adapted from third-party projects under their own licenses; see NOTICE for the full attribution.

Acknowledgements

DeepSpec builds on the ideas and code of several excellent open-source projects:

• SpecForge (Apache-2.0) — the overall training framework and Eagle3 implementation; portions of the Eagle3 modeling, loss, optimizer, attention, and evaluation code are adapted from it. Adapted files carry an in-file attribution comment, and the full notice is recorded in NOTICE.
• DFlash (MIT) — the DFlash draft-model design and training recipe.
• Qwen3 and Gemma — the target model families supported in this repo.

We thank the authors and maintainers of these projects. Contributions of new algorithms are welcome.