tsavage68/chat_1000STEPS_1e7rate_SFT_SFT
The tsavage68/chat_1000STEPS_1e7rate_SFT_SFT model is a 7 billion parameter language model, fine-tuned from Meta's Llama-2-7b-chat-hf architecture. It was trained for 1000 steps with a low learning rate of 1e-7, achieving a final validation loss of 1.2866. This model is a specialized iteration of Llama-2-7b-chat-hf, potentially optimized for specific conversational or instruction-following tasks, though the exact dataset and primary differentiator are not specified.
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Model Overview
The tsavage68/chat_1000STEPS_1e7rate_SFT_SFT is a 7 billion parameter language model derived from the meta-llama/Llama-2-7b-chat-hf architecture. It has undergone a supervised fine-tuning (SFT) process, indicated by its name and base model, suggesting an optimization for chat or instruction-following applications. The model was trained for 1000 steps with a notably low learning rate of 1e-7, which can contribute to stable training and fine-grained adjustments.
Training Details
During its 1000-step training, the model utilized a batch size of 4, with a gradient accumulation of 2, resulting in an effective total batch size of 8. The Adam optimizer was employed, and a cosine learning rate scheduler with 100 warmup steps was used. The training concluded with a validation loss of 1.2866, indicating a stable learning process over the 1000 steps.
Key Characteristics
- Base Model: Fine-tuned from Llama-2-7b-chat-hf.
- Parameter Count: 7 billion parameters.
- Training Steps: 1000 steps with a learning rate of 1e-7.
- Validation Loss: Achieved 1.2866.
Limitations
As per the provided information, details regarding the specific training dataset, intended uses, and limitations are not available. Users should exercise caution and conduct further evaluation to determine its suitability for particular applications.