HanningZhang/Llama3-sft-more-corr-rr60k-2ep
HanningZhang/Llama3-sft-more-corr-rr60k-2ep is an 8 billion parameter language model, fine-tuned from the Llama 3 architecture. This model is a result of supervised fine-tuning (SFT) with additional correction steps and a specific training regime (rr60k-2ep). Its primary characteristics and intended use cases are not explicitly detailed in the provided model card, indicating a need for further information regarding its specific optimizations or performance differentiators.
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Model Overview
HanningZhang/Llama3-sft-more-corr-rr60k-2ep is an 8 billion parameter language model based on the Llama 3 architecture. The model has undergone supervised fine-tuning (SFT) with a focus on "more corr" (likely referring to corrections) and a training regimen denoted as "rr60k-2ep". The specific details regarding the training data, procedure, and evaluation metrics are currently marked as "More Information Needed" in the model card.
Key Characteristics
- Base Model: Llama 3 architecture
- Parameter Count: 8 billion parameters
- Training Method: Supervised Fine-Tuning (SFT) with additional correction steps
- Context Length: 8192 tokens
Intended Use Cases
Due to the limited information in the provided model card, the specific direct and downstream use cases for this model are not explicitly defined. Users should consult updated documentation for details on its intended applications, performance benchmarks, and any known biases or limitations. Further information is needed to determine its suitability for particular tasks or to compare its performance against other Llama 3 variants.