tsavage68/chat_500STEPS_1e5rate_SFT
The tsavage68/chat_500STEPS_1e5rate_SFT model is a 7 billion parameter language model, fine-tuned from Meta's Llama-2-7b-chat-hf architecture with a 4096 token context length. This model underwent 500 training steps with a low learning rate of 1e-06, resulting in a final validation loss of 0.3160. It is intended for chat-based applications, leveraging its Llama-2 foundation for conversational tasks.
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Model Overview
The tsavage68/chat_500STEPS_1e5rate_SFT is a 7 billion parameter language model, fine-tuned from the meta-llama/Llama-2-7b-chat-hf base. It was trained for 500 steps using a supervised fine-tuning (SFT) approach, achieving a final validation loss of 0.3160. The training utilized a learning rate of 1e-06 and a cosine learning rate scheduler with 100 warmup steps.
Key Training Details
- Base Model: Llama-2-7b-chat-hf
- Training Steps: 500
- Learning Rate: 1e-06
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Batch Size: 4 (effective total batch size of 8 with gradient accumulation)
- Validation Loss: 0.3160
Potential Use Cases
Given its Llama-2-chat foundation and fine-tuning, this model is likely suitable for:
- General-purpose conversational AI
- Chatbot development
- Interactive text generation tasks