tiny-random-LlamaForCausalLM
| Property | Value |
|---|---|
| Parameter Count | 1.03M parameters |
| Model Type | Text Generation |
| Architecture | LLaMA-based |
| Tensor Type | F32 |
| Downloads | 1,325,830 |
| Research Paper | Environmental Impact Paper |
What is tiny-random-LlamaForCausalLM?
tiny-random-LlamaForCausalLM is a compact implementation of the LLaMA architecture, designed for efficient text generation tasks. With just 1.03M parameters, it represents a lightweight alternative to larger language models while maintaining useful text generation capabilities. The model utilizes F32 tensor precision and has gained significant traction with over 1.3 million downloads.
Implementation Details
The model is built using the Transformers library and implements the LLaMA architecture in PyTorch. It's optimized for text-generation-inference and supports deployment through Inference Endpoints.
- Built on PyTorch framework
- Implements Safetensors for secure model loading
- Supports text-generation-inference optimization
- Compatible with Hugging Face's Transformers library
Core Capabilities
- Text generation and completion tasks
- Efficient inference with F32 precision
- Deployment-ready through various endpoints
- Minimal resource requirements due to small parameter count
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its extremely compact size (1.03M parameters) while still implementing the LLaMA architecture, making it ideal for testing and lightweight deployments where resource efficiency is crucial.
Q: What are the recommended use cases?
The model is best suited for development testing, educational purposes, and scenarios where a lightweight LLaMA implementation is needed. It's particularly useful for prototyping and understanding LLaMA architecture without the computational overhead of larger models.
