AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b10000_s0
The AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b10000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model was trained on the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_cap_b10000_s0 dataset, suggesting a specialization in code-related tasks. With a context length of 8192 tokens, it is designed for applications requiring processing of moderately long sequences.
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Model Overview
The AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b10000_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_cap_b10000_s0 dataset, indicating a focus on code-related data.
Training Details
The model underwent a fine-tuning process with the following key hyperparameters:
- Learning Rate: 1e-05
- Batch Size: A total training batch size of 64 (2 per device with 8 gradient accumulation steps across 4 GPUs).
- Optimizer: ADAMW_TORCH with default betas and epsilon.
- Scheduler: Cosine learning rate scheduler with 0.03 warmup steps.
- Epochs: Trained for 1 epoch.
Framework Versions
Training was conducted using:
- Transformers 5.7.0
- Pytorch 2.13.0+cu130
- Datasets 4.0.0
- 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