Replicate API exposes Mamba 370M through model ID adirik/mamba-370m. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-05-22. Next refresh: weekly.
Quick Start
- 1
- 2Use the Replicate API SDK or REST API to call
adirik/mamba-370m— see the documentation for request format.
Code Examples
pip install replicateREPLICATE_API_TOKENadirik/mamba-370mReplicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
import replicate
# reads REPLICATE_API_TOKEN from env
# adirik/mamba-370m format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"adirik/mamba-370m",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Pricing on Replicate API
Capabilities
No model capability flags are currently sourced.
About Mamba 370M
Mamba 370M is a 370-million parameter large language model leveraging a state-space model (SSM) architecture, which differentiates it from traditional transformer models by eschewing attention and MLP blocks in favor of linear scaling with sequence length [6][9]. This design ensures efficient processing of lengthy sequences and is optimized for parallel GPU processing [6]. Notable for its text generation capabilities, Mamba 370M is also utilized for Japanese language processing [10], though the details of its training data vary, with some mentioning the Pile dataset [1].