tsavage68/chat_150STEPS_1e6rate_SFT
The tsavage68/chat_150STEPS_1e6rate_SFT model is a 7 billion parameter instruction-tuned causal language model, fine-tuned from meta-llama/Llama-2-7b-chat-hf. It was trained with a learning rate of 1e-06 over 150 steps, achieving a validation loss of 0.3523. This model is designed for chat-based applications, leveraging its Llama-2 base for conversational tasks.
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Model Overview
The tsavage68/chat_150STEPS_1e6rate_SFT is a 7 billion parameter language model, fine-tuned from the established meta-llama/Llama-2-7b-chat-hf architecture. This model underwent a specific supervised fine-tuning (SFT) process, indicated by its name, with a focus on chat-oriented applications.
Training Details
The model was trained using the following key hyperparameters:
- Base Model: meta-llama/Llama-2-7b-chat-hf
- Learning Rate: 1e-06
- Training Steps: 150
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Batch Size: A total training batch size of 8 (train_batch_size: 4, gradient_accumulation_steps: 2)
- Validation Loss: Achieved 0.3523 at the end of training.
Intended Use
Given its Llama-2-chat base and fine-tuning process, this model is primarily suited for conversational AI tasks. While specific use cases are not detailed in the original documentation, its foundation suggests utility in chatbots, interactive agents, and dialogue systems.