AmberYifan/capsd-marin-8b-base-code_dsir_b4000_s0
The AmberYifan/capsd-marin-8b-base-code_dsir_b4000_s0 model is an 8 billion parameter language model, fine-tuned from the marin-community/marin-8b-base architecture. This model is specifically optimized for code-related tasks, having been trained on the capsd_marin-8b-base-n80000-opc__mix_code_dsir_b4000_s0 dataset. It is designed to excel in scenarios requiring code generation, understanding, or manipulation, leveraging its specialized training for improved performance in programming contexts.
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Model Overview
The AmberYifan/capsd-marin-8b-base-code_dsir_b4000_s0 is an 8 billion parameter language model, building upon the marin-community/marin-8b-base architecture. This iteration has undergone a specialized fine-tuning process, focusing on code-related tasks.
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.
- Specialized Training: The model was fine-tuned on the
capsd_marin-8b-base-n80000-opc__mix_code_dsir_b4000_s0dataset, indicating a strong emphasis on code-centric data.
Training Details
The fine-tuning process involved specific hyperparameters:
- Learning Rate: 1e-05
- Optimizer: ADAMW_TORCH
- Scheduler: Cosine learning rate scheduler with 0.03 warmup steps.
- Epochs: Trained for 1 epoch.
- Batch Size: A total training batch size of 64 was used across 4 GPUs.
Intended Use Cases
Given its specialized training on a code-focused dataset, this model is primarily intended for applications requiring:
- Code generation
- Code completion
- Code understanding and analysis
Further details on specific use cases and limitations are expected to be provided by the model developers.