CharlesLi/llama_2_sky_safe_o1_llama_3_8B_default_1000_500_full
The CharlesLi/llama_2_sky_safe_o1_llama_3_8B_default_1000_500_full model is a 7 billion parameter language model fine-tuned from Meta's Llama-2-7b-chat-hf. This model was fine-tuned on a generator dataset, achieving a loss of 0.7590 on the evaluation set. It is intended for generative tasks, building upon the Llama 2 architecture with a 4096 token context length.
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Model Overview
This model, llama_2_sky_safe_o1_llama_3_8B_default_1000_500_full, is a fine-tuned variant of the Meta Llama-2-7b-chat-hf architecture. It features 7 billion parameters and was specifically trained on a generator dataset, achieving an evaluation loss of 0.7590. The base model, Llama-2-7b-chat-hf, is known for its conversational capabilities, and this fine-tune aims to adapt it for specific generative applications.
Key Training Details
The model was trained using the following hyperparameters:
- Learning Rate: 2e-05
- Batch Size: 4 (train), 4 (eval)
- Gradient Accumulation Steps: 2, leading to a total train batch size of 32
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Scheduler: Cosine learning rate scheduler with 0.1 warmup ratio
- Epochs: 1
Potential Use Cases
Given its fine-tuning on a generator dataset, this model is likely suitable for:
- Text Generation: Creating coherent and contextually relevant text based on prompts.
- Content Creation: Assisting in drafting various forms of written content.
Limitations
As noted in the original model card, more information is needed regarding its specific intended uses, limitations, and the exact nature of the training and evaluation data. Users should exercise caution and conduct further testing to determine its suitability for specific applications.