sstoica12/acquisition_metamath_llama_instruct-3_1-8b-math_diversity_500_combined_openr1math
The sstoica12/acquisition_metamath_llama_instruct-3_1-8b-math_diversity_500_combined_openr1math is an 8 billion parameter instruction-tuned causal language model. This model is designed for general language tasks, though specific differentiators and primary use cases are not detailed in the provided information. It is based on the Llama architecture and has a context length of 32768 tokens.
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Model Overview
This model is an 8 billion parameter instruction-tuned causal language model, developed by sstoica12. It is based on the Llama architecture and supports a context length of 32768 tokens. The model card indicates it is a Hugging Face Transformers model, automatically generated upon being pushed to the Hub.
Key Characteristics
- Model Type: Instruction-tuned causal language model.
- Parameter Count: 8 billion parameters.
- Context Length: 32768 tokens.
- Architecture: Llama-based.
Current Status and Limitations
As per the provided model card, many details regarding its development, funding, specific language support, license, and fine-tuning origins are marked as "More Information Needed." Consequently, specific direct or downstream use cases, as well as detailed information on bias, risks, and limitations, are not yet available. Users are advised to be aware of these missing details and the general risks associated with LLMs.
Getting Started
The model card includes a section for code to get started, but the actual code snippet is currently marked as "More Information Needed."
Training and Evaluation
Details concerning training data, preprocessing, hyperparameters, speeds, sizes, times, and evaluation metrics or results are also marked as "More Information Needed." This includes information on environmental impact, hardware, and carbon emissions.