AmberYifan/capsd-marin-8b-base-code_qurating_b1000_s0
AmberYifan/capsd-marin-8b-base-code_qurating_b1000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model is specifically trained on the capsd_marin-8b-base-n80000-opc__mix_code_qurating_b1000_s0 dataset, indicating a specialization in code-related tasks. With a context length of 8192 tokens, it is designed for applications requiring code understanding and generation.
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Model Overview
AmberYifan/capsd-marin-8b-base-code_qurating_b1000_s0 is an 8 billion parameter language model, fine-tuned from the marin-community/marin-8b-base architecture. This model has been specifically trained on the capsd_marin-8b-base-n80000-opc__mix_code_qurating_b1000_s0 dataset, suggesting a strong focus on code-related tasks and potentially code quality or curation.
Key Training Details
- Base Model: Fine-tuned from marin-community/marin-8b-base.
- Dataset: Trained on
capsd_marin-8b-base-n80000-opc__mix_code_qurating_b1000_s0. - Hyperparameters:
- Learning Rate: 1e-05
- Optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-08
- Scheduler: Cosine with 0.03 warmup steps
- Epochs: 1
- Context Length: 8192 tokens.
Potential Use Cases
Given its fine-tuning on a code-centric dataset, this model is likely suitable for:
- Code generation and completion.
- Code analysis and understanding.
- Assisting with code review or quality assessment tasks.