speculative-decodinglisted

# Speculative Decoding: Accelerating LLM Inference ## When to Use This Skill Use Speculative Decoding when you need to: - **Speed up inference** by 1.5-3.6× without quality loss - **Reduce latency** for real-time applications (chatbots, code generation) - **Optimize throughput** for high-volume serving - **Deploy efficiently** on limited hardware - **Generate faster** without changing model architecture **Key Techniques**: Draft model speculative decoding, Medusa (multiple heads), Lookahead Decoding (Jacobi iteration) **Papers**: Medusa (arXiv 2401.10774), Lookahead Decoding (ICML 2024), Speculative Decoding Survey (ACL 2024) ## Installation ```bash # Standard speculative decoding (transformers) pip install transformers accelerate # Medusa (multiple decoding heads) git clone https://github.com/FasterDecoding/Medusa cd Medusa pip install -e . # Lookahead Decoding git clone https://github.com/hao-ai-lab/LookaheadDecoding cd LookaheadDecoding pip install -e . # Optional: vLLM with speculative decoding pip install vllm ``` ## Quick Start ### Basic Speculative Decoding (Draft Model) ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Load target model (large, slow) target_model = AutoModelForCausalLM.from_pretrained( "meta-llama/Llama-2-70b-hf", device_map="auto", torch_dtype=torch.float16 ) # Load draft model (small, fast) draft_model = AutoModelForCausalLM.from_pretrained( "meta-llama/Llama-2-7b-hf", device_map="auto", t