deepseek-ai/DeepSpec

Health v2 · maintenance only

Flags: no_releases young

Adoption not part of the score

7041 stars · 660 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DeepSpec is a full-stack Python codebase from DeepSeek for training and evaluating draft models used in speculative decoding of large language models. It provides data preparation pipelines, training scripts, evaluation benchmarks, and released checkpoints for algorithms like Eagle3.

Use cases

• train a draft model for speculative decoding
• evaluate speculative decoding acceptance rates on benchmarks
• speed up LLM inference with a trained draft model
• prepare target model output caches for draft training
• reproduce speculative decoding research results

When to choose

• you want to train or benchmark speculative decoding draft models
• you need released Eagle3-style checkpoints for Qwen or Gemma targets
• you have multi-GPU hardware and want a complete training-to-evaluation pipeline

When to avoid

• you only need to run inference without speculative decoding
• you lack GPU resources or the large storage required for target caches
• you need a plug-and-play inference engine rather than a training framework

Facets

library · maturity active

llm-training llm-inference machine-learning benchmarking etl large-language-models machine-learning deep-learning developer-tools python speculative-decoding draft-models eagle3 model-training inference-optimization llm-acceleration linux gpu docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="deepseek-ai/DeepSpec")