AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b10000_s0
The AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b10000_s0 model is an 8 billion parameter language model fine-tuned from marin-community/marin-8b-base. This model was specifically trained on the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_ppl_b10000_s0 dataset, indicating a specialization towards code-related tasks. It leverages a context length of 8192 tokens, making it suitable for processing moderately long code sequences. Its fine-tuning on a code-centric dataset suggests its primary strength lies in code generation, completion, or understanding.
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Model Overview
The AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b10000_s0 is an 8 billion parameter language model derived from the marin-community/marin-8b-base architecture. This model has undergone a specific fine-tuning process, utilizing the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_ppl_b10000_s0 dataset.
Training Details
The fine-tuning process involved a single epoch with a learning rate of 1e-05. Key training hyperparameters include:
- Optimizer: AdamW_Torch with betas=(0.9, 0.999) and epsilon=1e-08.
- Scheduler: Cosine learning rate scheduler with 0.03 warmup steps.
- Batch Size: A total train batch size of 64 (2 per device across 4 GPUs with 8 gradient accumulation steps).
Potential Use Cases
Given its fine-tuning on a code-oriented dataset, this model is likely optimized for tasks such as:
- Code Generation: Producing new code snippets based on prompts.
- Code Completion: Suggesting the next lines or blocks of code.
- Code Understanding: Assisting with code analysis or explanation.
Limitations
The model card indicates that more information is needed regarding its detailed description, intended uses, limitations, and specific training/evaluation data. Users should consider this when deploying the model for critical applications.