AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b12000_s0
AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b12000_s0 is an 8 billion parameter language model fine-tuned from marin-community/marin-8b-base. This model was trained on a specific code-centric dataset, capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_ppl_b12000_s0, suggesting an optimization for code-related tasks. It utilizes a context length of 8192 tokens and was fine-tuned with a learning rate of 1e-05 over a single epoch.
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Model Overview
AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b12000_s0 is an 8 billion parameter language model, fine-tuned from the marin-community/marin-8b-base architecture. This model was specifically trained on the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_ppl_b12000_s0 dataset, indicating a focus on code-related applications and potentially improved performance in programming contexts.
Training Details
The fine-tuning process involved a single epoch with a learning rate of 1e-05, using an AdamW optimizer and a cosine learning rate scheduler. The training was conducted on a multi-GPU setup with 4 devices, a total batch size of 64, and a gradient accumulation of 8 steps. The model was developed using Transformers 5.7.0, Pytorch 2.13.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.
Potential Use Cases
Given its fine-tuning on a code-centric dataset, this model is likely suitable for tasks such as:
- Code generation
- Code completion
- Code summarization
- Debugging assistance
Further evaluation is needed to confirm specific performance metrics and optimal applications.