CharlesLi/llama_2_sky_safe_o1_llama_3_70B_reflect_4000_500_full
The CharlesLi/llama_2_sky_safe_o1_llama_3_70B_reflect_4000_500_full model is a 7 billion parameter language model, fine-tuned from Meta's Llama-2-7b-chat-hf. It is designed for general language generation tasks, leveraging a 4096-token context length. This model focuses on improving performance through specific fine-tuning on a generator dataset, achieving a validation loss of 0.6591.
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Model Overview
This model, llama_2_sky_safe_o1_llama_3_70B_reflect_4000_500_full, is a 7 billion parameter language model derived from meta-llama/Llama-2-7b-chat-hf. It has been specifically fine-tuned on a generator dataset, indicating an optimization for text generation tasks. The training process involved a single epoch with a learning rate of 2e-05 and a total batch size of 32 across 4 GPUs.
Key Training Details
- Base Model: meta-llama/Llama-2-7b-chat-hf
- Parameter Count: 7 billion
- Context Length: 4096 tokens
- Training Loss: Achieved a final validation loss of 0.6591.
- Hyperparameters: Utilized Adam optimizer with betas=(0.9, 0.999) and epsilon=1e-08, a cosine learning rate scheduler with a 0.1 warmup ratio.
Potential Use Cases
Given its fine-tuning on a 'generator dataset', this model is likely suitable for applications requiring:
- Text generation
- Content creation
- Conversational AI (building upon its Llama-2-chat base)
Further details on specific intended uses and limitations are not provided in the original model card, suggesting a general-purpose fine-tune for generative tasks.