tsavage68/chat_150STEPS_1e7rate_01beta_DPO
tsavage68/chat_150STEPS_1e7rate_01beta_DPO is a 7 billion parameter language model fine-tuned from Meta's Llama-2-7b-chat-hf. This model was trained using a learning rate of 1e-07 over 150 steps, focusing on chat-based interactions. Its development involved specific DPO training hyperparameters, aiming to refine conversational responses.
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Model Overview
The tsavage68/chat_150STEPS_1e7rate_01beta_DPO is a 7 billion parameter language model derived from the meta-llama/Llama-2-7b-chat-hf architecture. This model has undergone a fine-tuning process, though the specific dataset used for this training is not detailed in the provided information.
Training Details
The model was trained with a focus on chat applications, utilizing a learning rate of 1e-07 and a total of 150 training steps. Key hyperparameters included a train_batch_size of 4, gradient_accumulation_steps of 2, and an Adam optimizer with specific beta values. The training process involved a cosine learning rate scheduler with 100 warmup steps. Evaluation metrics during training showed a final loss of 0.6933, with rewards/chosen at -0.0025 and rewards/rejected at -0.0022, indicating a slight preference for chosen responses.
Key Characteristics
- Base Model: Fine-tuned from Llama-2-7b-chat-hf.
- Parameter Count: 7 billion parameters.
- Training Steps: 150 steps with a low learning rate (1e-07).
- Optimization: Adam optimizer with specific settings.
Potential Use Cases
Given its origin from a chat-optimized base model and DPO training, this model is likely intended for conversational AI applications. However, without further details on the fine-tuning dataset or specific performance benchmarks beyond training loss, its precise strengths and limitations for various use cases remain to be fully explored.