karimasbar/results
The karimasbar/results model is a fine-tuned version of Meta's Llama-2-7b-chat-hf, a 7 billion parameter causal language model. This model has been adapted through specific training hyperparameters, including a learning rate of 0.0002 and 5000 training steps. Its primary application is for tasks aligned with the fine-tuning objectives, though specific use cases and limitations require further information.
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Model Overview
The karimasbar/results model is a fine-tuned iteration of the meta-llama/Llama-2-7b-chat-hf base model. Llama-2-7b-chat-hf is a 7 billion parameter conversational language model developed by Meta. This fine-tuned version was trained using specific hyperparameters over 5000 steps.
Training Details
The fine-tuning process utilized the following key hyperparameters:
- Learning Rate: 0.0002
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Batch Sizes:
train_batch_sizeof 4,eval_batch_sizeof 8 - Gradient Accumulation: 4 steps, leading to a
total_train_batch_sizeof 16 - Scheduler: Constant learning rate scheduler with a warmup ratio of 0.03
- Training Steps: 5000
Intended Uses & Limitations
Specific intended uses and limitations for this fine-tuned model are not detailed in the provided information. Developers should refer to the capabilities and limitations of the base Llama-2-7b-chat-hf model as a starting point, and conduct further evaluation to understand the specific performance characteristics of this fine-tuned version for their particular applications. The model's performance will be influenced by the undisclosed "None" dataset used for fine-tuning.