shuoxing/llama3-8b-full-pretrain-c4-1m-en
The shuoxing/llama3-8b-full-pretrain-c4-1m-en model is an 8 billion parameter Llama 3 architecture, fine-tuned from Meta-Llama-3-8B-Instruct. This model has been specifically adapted using the c4_1m_en dataset. It is designed for English language tasks, leveraging its Llama 3 base for general language understanding and generation.
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shuoxing/llama3-8b-full-pretrain-c4-1m-en Overview
This model is an 8 billion parameter language model, fine-tuned from the meta-llama/Meta-Llama-3-8B-Instruct base model. The fine-tuning process utilized the c4_1m_en dataset, indicating a focus on English language text generation and comprehension tasks. The training involved specific hyperparameters including a learning rate of 1e-05, a total batch size of 8 across 8 devices, and 3 epochs of training.
Key Characteristics
- Base Model: Meta-Llama-3-8B-Instruct
- Parameter Count: 8 billion
- Fine-tuning Dataset: c4_1m_en
- Training Framework: Transformers 5.6.0, Pytorch 2.12.0+cu130, Datasets 4.0.0, Tokenizers 0.22.2
Intended Use Cases
Given its fine-tuning on an English dataset, this model is suitable for a range of English natural language processing applications. Potential uses include text generation, summarization, question answering, and other tasks where a robust understanding and generation of English text is required. Users should be aware that specific performance metrics and detailed limitations are not provided in the original model card, suggesting further evaluation may be necessary for critical applications.