Xinging/llama2-7b_sft_0.4_ratio_alpaca_gpt4_proj_by_comprehensive_ntrain_126676_default
Xinging/llama2-7b_sft_0.4_ratio_alpaca_gpt4_proj_by_comprehensive_ntrain_126676_default is a 7 billion parameter language model fine-tuned from Meta's Llama-2-7b-hf. This model was specifically trained on the 0.4_ratio_alpaca_gpt4_proj_by_comprehensive_ntrain_126676 dataset, suggesting an optimization for tasks related to instruction following and conversational AI, potentially leveraging a mix of Alpaca and GPT-4 generated data. Its primary use case is likely in applications requiring robust instruction-tuned responses, building upon the strong base capabilities of the Llama 2 architecture.
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Overview
This model, Xinging/llama2-7b_sft_0.4_ratio_alpaca_gpt4_proj_by_comprehensive_ntrain_126676_default, is a 7 billion parameter language model derived from meta-llama/Llama-2-7b-hf. It has undergone specific fine-tuning on the 0.4_ratio_alpaca_gpt4_proj_by_comprehensive_ntrain_126676 dataset. This fine-tuning process indicates an emphasis on enhancing the model's ability to follow instructions and engage in conversational tasks, likely by incorporating data from both Alpaca and GPT-4 projects.
Key Capabilities
- Instruction Following: Enhanced ability to understand and execute instructions due to specialized fine-tuning.
- Conversational AI: Potentially improved performance in dialogue systems and interactive applications.
- Llama 2 Base: Benefits from the robust foundational capabilities of the Llama 2 architecture.
Good for
- Applications requiring a 7B model with strong instruction-following capabilities.
- Developing chatbots or conversational agents.
- Tasks that benefit from models trained on diverse instruction-based datasets.
Training Details
The model was trained with a learning rate of 2e-05, a total batch size of 128 (across 4 GPUs), and for 3 epochs. The optimizer used was adamw_torch with a cosine learning rate scheduler and a warmup ratio of 0.03.