tsavage68/chat_300STEPS_1e7rate_SFT
The tsavage68/chat_300STEPS_1e7rate_SFT is a 7 billion parameter language model, fine-tuned from meta-llama/Llama-2-7b-chat-hf. This model was trained with a low learning rate of 1e-07 over 300 steps, focusing on incremental refinement. Its primary characteristic is its specific training regimen, which may lead to specialized conversational abilities, though the exact dataset and intended uses are not specified.
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Model Overview
The tsavage68/chat_300STEPS_1e7rate_SFT is a 7 billion parameter language model, derived from the meta-llama/Llama-2-7b-chat-hf architecture. It has undergone a specific fine-tuning process, although the dataset used for this training is currently unspecified.
Training Details
The model was trained using a learning rate of 1e-07 over 300 steps, with a cosine learning rate scheduler and 100 warmup steps. Key hyperparameters included a train_batch_size of 4 (resulting in a total_train_batch_size of 8 with gradient accumulation) and an Adam optimizer. The training process showed a consistent reduction in loss, concluding with a validation loss of 1.4992.
Key Characteristics
- Base Model: Fine-tuned from
meta-llama/Llama-2-7b-chat-hf. - Parameter Count: 7 billion parameters.
- Context Length: 4096 tokens.
- Training Regimen: Features a very low learning rate (
1e-07) and a limited number of training steps (300), suggesting a focused, incremental fine-tuning approach.
Potential Use Cases
Given its fine-tuning from a chat-optimized base model, this model is likely intended for conversational AI applications. The specific training parameters might indicate an optimization for nuanced responses or a particular domain, though further details on the training data would be necessary to confirm this.