prompt-agnostic-language-models/Llama-1B_single_longer
The Llama 3.2 1B model by Meta is a 1.23 billion parameter, multilingual, instruction-tuned generative language model with a 32,768 token context length. Optimized for dialogue use cases, it excels in agentic retrieval, summarization, and mobile AI applications. This model is part of the Llama 3.2 collection, utilizing an optimized transformer architecture and trained on up to 9 trillion tokens of publicly available data.
Loading preview...
Model Overview
prompt-agnostic-language-models/Llama-1B_single_longer is a 1.23 billion parameter model from Meta's Llama 3.2 collection. This instruction-tuned, multilingual LLM is designed for text-in/text-out generative tasks, leveraging an optimized transformer architecture. It was trained on up to 9 trillion tokens of diverse public data, with a knowledge cutoff of December 2023. A key feature is its use of Grouped-Query Attention (GQA) for enhanced inference scalability and a substantial context length of 32,768 tokens.
Key Capabilities
- Multilingual Support: Officially supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai, with broader training across other languages.
- Dialogue Optimization: Specifically fine-tuned for conversational AI, agentic retrieval, and summarization tasks.
- Quantization Schemes: Features advanced quantization methods like SpinQuant and QLoRA, significantly reducing model size and memory footprint while boosting inference speed on constrained devices (e.g., mobile).
- Performance: Demonstrates competitive performance on various benchmarks, including MMLU, AGIEval, and long-context tasks, outperforming many open-source and closed chat models in its class.
Good For
- Commercial and Research Use: Suitable for a wide range of applications in multiple languages.
- Assistant-like Chat Applications: Optimized for interactive dialogue and conversational agents.
- Agentic Systems: Ideal for knowledge retrieval, summarization, and query/prompt rewriting.
- Constrained Environments: The 1B parameter size and efficient quantization make it particularly well-suited for on-device AI applications, such as mobile writing assistants, where compute resources are limited.