The fastest way to get this model running locally is via Docker.
Follow the guidelines below to continue.
Following this guide to the end unlocks everything you ever wanted to get out of this environment.
The **Llama-Nemotron-Embed-1B-v2** is a compact, openâsource embedding model that leverages the proven Llama architecture while focusing on efficient text representation. It delivers *stateâofâtheâart* performance on semantic similarity tasks despite its modest **1âŻB** parameter count, making it ideal for edge devices and lowâresource environments. The model supports up to **2048** token context length and produces **768âdimensional** embeddings, which balance granularity with computational efficiency. Training was performed on a diverse, **webâscale corpus**, enabling robust understanding of multiple languages and domains without sacrificing inference speed. A quick comparison in the table below highlights how its **parameter efficiency** and **embedding quality** stack up against similar open models.
| Parameters | 1âŻB |
| Embedding Dim | 768 |
| Context Length | 2048 tokens |
| Training Data | Webâscale corpus |
| Model Size (approx.) | 2âŻGB |
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