tsavage68/chat_1000STEPS_1e6rate_SFT_SFT
The tsavage68/chat_1000STEPS_1e6rate_SFT_SFT model is a 7 billion parameter language model fine-tuned from Meta's Llama-2-7b-chat-hf. This model was trained for 1000 steps with a low learning rate of 1e-06, achieving a final validation loss of 0.3054. It is a specialized fine-tune focusing on conversational capabilities inherited from its base model, with specific training parameters that suggest a targeted refinement process.
Loading preview...
Model Overview
This model, tsavage68/chat_1000STEPS_1e6rate_SFT_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, indicated by its name and training parameters, to refine its conversational abilities.
Training Details
The model was fine-tuned over 1000 training steps using a learning rate of 1e-06, a batch size of 4 (total batch size of 8 with gradient accumulation), and the Adam optimizer. The training process resulted in a final validation loss of 0.3054, suggesting a stable and effective fine-tuning run. The low learning rate and specific step count indicate a focused effort to adapt the base Llama 2 Chat model.
Key Characteristics
- Base Model: Fine-tuned from
meta-llama/Llama-2-7b-chat-hf. - Parameter Count: 7 billion parameters.
- Training Steps: 1000 steps with a learning rate of 1e-06.
- Performance: Achieved a validation loss of 0.3054.
Intended Use
Given its origin as a fine-tuned chat model, it is primarily intended for conversational AI applications. The specific training parameters suggest a focus on refining existing chat capabilities rather than introducing entirely new ones. Users should consider its Llama 2 Chat heritage for typical use cases like dialogue generation, question answering, and interactive text generation.