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llama-nemotron-embed-1b-v2 No-Internet Version

llama-nemotron-embed-1b-v2 No-Internet Version

Using Docker is the absolute quickest way to install this model on your local machine.

Use the instructions provided below to complete the setup.

Hands-free setup: the system self-downloads the heavy model files.

The smart installation system will instantly find the perfect configuration for your specific hardware.

🔍 Hash-sum: 71a4ab484048b724627acc8f71b80bac | 🕓 Last update: 2026-06-28
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  • CPU: 8-core / 16-thread recommended for orchestration
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk: 150+ GB for high-context vector database storage
  • Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

The **Llama-Nemotron-Embed-1B-v2** is a compact, open‑source embedding model that leverages the proven Llama architecture while focusing on efficient text representation. It delivers *state‑of‑the‑art* performance on semantic similarity tasks despite its modest **1 B** parameter count, making it ideal for edge devices and low‑resource environments. The model supports up to **2048** token context length and produces **768‑dimensional** embeddings, which balance granularity with computational efficiency. Training was performed on a diverse, **web‑scale 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 1 B
Embedding Dim 768
Context Length 2048 tokens
Training Data Web‑scale corpus
Model Size (approx.) 2 GB
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