AmberYifan/capsd-less-fast-opc-marin-8b-base-code_less_b2000_s0
AmberYifan/capsd-less-fast-opc-marin-8b-base-code_less_b2000_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__mix_code_less_b2000_s0 dataset, indicating a specialization in code-related tasks. It features an 8192 token context length, making it suitable for processing moderately long code sequences.
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Model Overview
This model, AmberYifan/capsd-less-fast-opc-marin-8b-base-code_less_b2000_s0, is an 8 billion parameter language model. It is a fine-tuned variant of the marin-community/marin-8b-base architecture, specifically adapted through further training.
Key Characteristics
- Base Model: Fine-tuned from
marin-community/marin-8b-base. - Parameter Count: 8 billion parameters.
- Context Length: Supports an 8192 token context window.
- Training Data: Fine-tuned on the
capsd_marin-8b-base-n80000-opc__mix_code_less_b2000_s0dataset, suggesting an emphasis on code-related data.
Training Details
The fine-tuning process utilized specific hyperparameters:
- Learning Rate: 1e-05
- Optimizer: ADAMW_TORCH with betas=(0.9, 0.999) and epsilon=1e-08
- Scheduler: Cosine learning rate scheduler with 0.03 warmup steps
- Epochs: Trained for 1 epoch
- Batch Size: A total train batch size of 64 (with 8 gradient accumulation steps across 4 GPUs).
Potential Use Cases
Given its fine-tuning on a code-centric dataset, this model is likely optimized for tasks such as:
- Code generation
- Code completion
- Code summarization
- Debugging assistance
Further evaluation is needed to confirm specific performance metrics and limitations.