StableLM
Product information, use cases, and access for StableLM.
Pricing information
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What is StableLM?
StableLM is an open-source language-model family developed by Stability AI for people who want to use, study, or adapt large language models. Its official repository documents multiple checkpoints and related conversational variants, including StableLM-Tuned-Alpha and StableVicuna.
The interaction depends on the selected checkpoint: a base model can serve as a starting point for downstream fine-tuning, while a tuned conversational model expects a structured system, user, and assistant prompt format. The models are hosted through Hugging Face, and the repository provides Transformers and PyTorch examples for inference alongside training configurations and evaluation material.
Key features of StableLM
Open model checkpoints
The official repository presents multiple StableLM language-model checkpoints for developers who want to inspect the family, run experiments, or use a checkpoint as the foundation for an application.
Downstream fine-tuning
The official documentation recommends using a base StableLM model for downstream fine-tuning, making it suitable for developers who need to adapt model behavior around task-specific data rather than only use a ready-made chat interface.
Structured chat prompts
The tuned versions use a prompt format organized around system, user, and assistant roles. This suits users who want to test conversational behavior or keep message boundaries explicit when integrating the model into an application.
Local inference and experimentation
The repository shows how to load a tokenizer and model with Transformers and PyTorch and generate text, while also pointing to Hugging Face resources. These materials support local inference, prototyping, and hands-on model experimentation.