CharlesLi/llama_2_rlhf_safe_llama_3_70B_default_500_full
CharlesLi/llama_2_rlhf_safe_llama_3_70B_default_500_full is a 7 billion parameter language model, fine-tuned from Meta's Llama-2-7b-chat-hf. This model has undergone fine-tuning on a specific generator dataset, achieving a loss of 0.9015 on its evaluation set. It is intended for applications requiring a Llama-2-based model with specific fine-tuning characteristics.
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Model Overview
This model, llama_2_rlhf_safe_llama_3_70B_default_500_full, is a fine-tuned variant of the meta-llama/Llama-2-7b-chat-hf base model. It features 7 billion parameters and was trained with a context length of 4096 tokens. The fine-tuning process involved a specific generator dataset, resulting in an evaluation loss of 0.9015.
Training Details
The model was trained using the following key hyperparameters:
- Learning Rate: 2e-05
- Batch Sizes:
train_batch_sizeof 4,eval_batch_sizeof 4 - Optimizer: Adam with default betas and epsilon
- Scheduler: Cosine learning rate scheduler with a 0.1 warmup ratio
- Epochs: 1
Key Characteristics
- Base Model: Fine-tuned from
meta-llama/Llama-2-7b-chat-hf. - Parameter Count: 7 billion parameters.
- Evaluation Performance: Achieved a loss of 0.9015 on its evaluation set, indicating its performance on the specific fine-tuning task.
Intended Use Cases
This model is suitable for applications that can leverage a Llama-2-7b-chat-hf derivative, particularly where its specific fine-tuning on a generator dataset aligns with the desired output characteristics. Developers should consider its base architecture and fine-tuning focus when integrating it into their projects.