tsavage68/chat_1000STEPS_1e7rate_01beta_DPO
tsavage68/chat_1000STEPS_1e7rate_01beta_DPO is a 7 billion parameter language model fine-tuned from meta-llama/Llama-2-7b-chat-hf. This model was trained using a learning rate of 1e-07 over 1000 steps, focusing on improving chat capabilities through a DPO (Direct Preference Optimization) approach. It demonstrates specific training metrics for rewards and log probabilities, indicating its optimization for generating preferred responses in conversational contexts.
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Model Overview
The tsavage68/chat_1000STEPS_1e7rate_01beta_DPO is a 7 billion parameter language model derived from the meta-llama/Llama-2-7b-chat-hf base model. It has undergone a fine-tuning process using Direct Preference Optimization (DPO) with a specific training regimen.
Key Training Details
- Base Model: meta-llama/Llama-2-7b-chat-hf
- Optimization Method: Direct Preference Optimization (DPO)
- Learning Rate: 1e-07
- Training Steps: 1000
- Batch Size: A total training batch size of 8 (train_batch_size: 4, gradient_accumulation_steps: 2)
- Optimizer: Adam with standard betas and epsilon
- Scheduler: Cosine learning rate scheduler with 100 warmup steps
Performance Metrics
During its 1000-step training, the model achieved a final validation loss of 0.6919. Key DPO-specific metrics include a rewards/accuracies score of 0.4637 and a rewards/margins of 0.0027, indicating its progress in aligning with preferred responses. The training focused on refining conversational outputs, as evidenced by the DPO methodology and the base model choice.
Intended Use
While specific intended uses and limitations are not detailed in the provided information, its fine-tuning from a chat-optimized Llama-2 variant suggests its suitability for conversational AI applications where response quality and preference alignment are important.