This is a minimal random-weight model based on the LlamaForCausalLM architecture, published on Hugging Face. It is intended for testing and development purposes, allowing developers to validate pipelines, tokenizers, and inference code without downloading full-scale models. It supports the Transformers library and can be used locally or via the Hugging Face inference ecosystem.
In the Foundation models & chat space, Tiny Random LlamaForCausalLM takes a focused approach. It focuses on needing a minimal random model for testing and debugging LlamaForCausalLM implementations without large compute requirements. Tiny Random LlamaForCausalLM is an open-source project aimed at machine learning developers. The project is open source (Open Source). It runs on the web, the command line, and API.
It is developed by Hugging Face (United States), and the product first shipped in 2023. Among its 3 catalogued features are Causal Language Modeling, Test Model, and Transformers Compatible.
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