CharlesLi/llama_2_llama_2_code_math_3_full
The CharlesLi/llama_2_llama_2_code_math_3_full is a 7 billion parameter language model, fine-tuned from Meta's Llama-2-7b-chat-hf. This model is specifically fine-tuned on a generator dataset, indicating an optimization for content generation tasks. It is designed for applications requiring a Llama-2-based model with enhanced generative capabilities, as suggested by its training on a 'generator dataset'.
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Model Overview
The CharlesLi/llama_2_llama_2_code_math_3_full is a 7 billion parameter language model, fine-tuned from the meta-llama/Llama-2-7b-chat-hf base model. This fine-tuning process utilized a specific "generator dataset," suggesting an optimization for tasks involving content creation or generation.
Key Training Details
- Base Model: meta-llama/Llama-2-7b-chat-hf
- Parameter Count: 7 billion
- Training Objective: Fine-tuned on a "generator dataset."
- Observed Loss: Achieved a loss of 0.5628 on the evaluation set.
- Hyperparameters:
- Learning Rate: 2e-05
- Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
- Epochs: 1
- Batch Size: 4 (train/eval), total 32 (train), 16 (eval)
Intended Use Cases
While specific intended uses are not detailed, the fine-tuning on a "generator dataset" implies suitability for tasks where the model needs to produce coherent and relevant text, potentially for creative writing, summarization, or other generative applications. Users should consider its Llama-2 lineage for general language understanding and generation tasks, with an emphasis on its fine-tuned generative capabilities.