Documentation

Getting Started

Predicting with LLMs

Agentic Flows

Text Embedding

Tokenization

Manage Models

Model Info

Speculative Decoding

Required Python SDK version: 1.2.0

Speculative decoding is a technique that can substantially increase the generation speed of large language models (LLMs) without reducing response quality. See Speculative Decoding for more info.

To use speculative decoding in lmstudio-python, simply provide a draftModel parameter when performing the prediction. You do not need to load the draft model separately.

import lmstudio as lms

main_model_key = "qwen2.5-7b-instruct"
draft_model_key = "qwen2.5-0.5b-instruct"

model = lms.llm(main_model_key)
result = model.respond(
    "What are the prime numbers between 0 and 100?",
    config={
        "draftModel": draft_model_key,
    }
)

print(result)
stats = result.stats
print(f"Accepted {stats.accepted_draft_tokens_count}/{stats.predicted_tokens_count} tokens")