zsjTiger/Llama-3.2-1B

TEXT GENERATIONPricing:Input $0.108 / Output $0.804Concurrent Unit Cost:1Model Size:1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 20, 2026License:llama3.2Architecture:Transformer Featherless Exclusive Cold

zsjTiger/Llama-3.2-1B is a 1 billion parameter multilingual large language model developed by Meta, based on an optimized transformer architecture. This instruction-tuned model is specifically optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. It supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, outperforming many open-source and closed chat models on common industry benchmarks. The model is designed for efficient fine-tuning, offering 2.4x faster training and 58% less memory usage when utilizing Unsloth.

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Llama-3.2-1B Overview

zsjTiger/Llama-3.2-1B is a 1 billion parameter model from Meta's Llama 3.2 collection, built on an optimized transformer architecture. This instruction-tuned version is designed for multilingual dialogue, excelling in agentic retrieval and summarization. It leverages Grouped-Query Attention (GQA) for improved inference scalability.

Key Capabilities & Features

  • Multilingual Support: Officially supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, with training on a broader set of languages.
  • Optimized for Dialogue: Specifically fine-tuned for conversational AI, retrieval, and summarization tasks.
  • Performance: Outperforms many other open-source and closed chat models on standard industry benchmarks.
  • Efficient Fine-tuning: When used with Unsloth, it enables 2.4x faster fine-tuning with 58% less memory consumption, making it accessible for free on platforms like Google Colab.
  • Architecture: Utilizes an auto-regressive language model with an optimized transformer architecture, incorporating supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) for alignment.

Good For

  • Multilingual Chatbots: Developing conversational agents in supported languages.
  • Agentic Retrieval Systems: Implementing systems that require information retrieval and synthesis.
  • Text Summarization: Generating concise summaries from various texts.
  • Resource-Efficient Fine-tuning: Developers looking to fine-tune a powerful model quickly and with limited GPU resources, especially via Unsloth's optimized methods.
  • Academic and Research Projects: Exploring multilingual LLM capabilities and fine-tuning techniques.

Finetunes
1 models