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Getting Started

Predicting with LLMs

Agentic Flows

Text Embedding

Tokenization

Model Info

List Downloaded Models

You can iterate through locally available models using the downloaded model listing methods.

The listing results offer .model() and .load_new_instance() methods, which allow the downloaded model reference to be converted in the full SDK handle for a loaded model.

Available Models on the LM Studio Server

import lmstudio as lms

downloaded = lms.list_downloaded_models()
llm_only = lms.list_downloaded_models("llm")
embedding_only = lms.list_downloaded_models("embedding")

for model in downloaded:
    print(model)

This will give you results equivalent to using lms ls in the CLI.

Example output:

DownloadedLlm(model_key='qwen2.5-7b-instruct-1m', display_name='Qwen2.5 7B Instruct 1M', architecture='qwen2', vision=False)
DownloadedEmbeddingModel(model_key='text-embedding-nomic-embed-text-v1.5', display_name='Nomic Embed Text v1.5', architecture='nomic-bert')