zededa/Llama-3.2-1B-Instruct

TEXT GENERATIONConcurrency Cost:1Model Size:1BQuant:BF16Ctx Length:32kPublished:Apr 7, 2026License:llama3.2Architecture:Transformer Cold

The zededa/Llama-3.2-1B-Instruct is a 1 billion parameter instruction-tuned causal language model developed by Meta, based on the Llama 3.2 architecture. Optimized for multilingual dialogue use cases, including agentic retrieval and summarization, it supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. This model features an optimized transformer architecture with Grouped-Query Attention and a 32768 token context length, outperforming many open-source and closed chat models on common benchmarks.

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Model Overview

zededa/Llama-3.2-1B-Instruct is a 1 billion parameter instruction-tuned model from Meta's Llama 3.2 collection. It is built on an optimized transformer architecture utilizing Grouped-Query Attention (GQA) for enhanced inference scalability and features a 32768 token context length. The instruction-tuned versions are aligned with human preferences through supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).

Key Capabilities

  • Multilingual Dialogue: Optimized for multilingual dialogue use cases, including agentic retrieval and summarization.
  • Supported Languages: Officially supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, with training on a broader collection of languages.
  • Performance: Outperforms many open-source and closed chat models on common industry benchmarks.
  • Architecture: Uses an auto-regressive language model with an optimized transformer architecture and Grouped-Query Attention.

Good For

  • Multilingual Applications: Ideal for applications requiring multilingual interaction and understanding.
  • Dialogue Systems: Suitable for building conversational AI agents, chatbots, and systems requiring dialogue capabilities.
  • Retrieval and Summarization: Optimized for tasks involving information retrieval and text summarization in various languages.
  • Finetuning: The model is designed to be finetuned, with tools like Unsloth offering accelerated finetuning with reduced memory usage.

Finetunes
2 models