CharlesLi/llama_2_sky_safe_o1_llama_3_70B_reflect_4000_1000_full
CharlesLi/llama_2_sky_safe_o1_llama_3_70B_reflect_4000_1000_full is a 7 billion parameter language model fine-tuned by CharlesLi, based on Meta's Llama-2-7b-chat-hf architecture. This model was fine-tuned on a generator dataset, achieving a validation loss of 0.7594 during its single-epoch training. It is intended for tasks requiring a Llama-2-7b-chat-hf base model with specific adaptations from its fine-tuning process.
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Model Overview
This model, llama_2_sky_safe_o1_llama_3_70B_reflect_4000_1000_full, is a fine-tuned variant of Meta's Llama-2-7b-chat-hf (7 billion parameters). It was developed by CharlesLi through a single-epoch training process on a specialized generator dataset.
Training Details
The fine-tuning utilized the following key hyperparameters:
- Learning Rate: 2e-05
- Batch Size: 4 (train and eval)
- Gradient Accumulation: 2 steps, resulting in a total effective batch size of 32
- Optimizer: Adam with standard betas and epsilon
- Scheduler: Cosine with a 0.1 warmup ratio
During training, the model achieved a validation loss of 0.7594 after 200 steps. The training was conducted using Transformers 4.44.2, Pytorch 2.4.1+cu121, Datasets 3.0.0, and Tokenizers 0.19.1.
Potential Use Cases
Given its foundation on Llama-2-7b-chat-hf and fine-tuning on a generator dataset, this model is likely suitable for applications that benefit from:
- Text generation tasks where the generator dataset's characteristics are relevant.
- Chatbot or conversational AI systems requiring a Llama-2 base with specific behavioral adjustments.
- Further experimentation or fine-tuning for domain-specific applications building upon its current training.