AmberYifan/capsdnum-marin-8b-base-code_cap_b2000_s0

TEXT GENERATIONConcurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Jul 16, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold

AmberYifan/capsdnum-marin-8b-base-code_cap_b2000_s0 is an 8 billion parameter language model fine-tuned from marin-community/marin-8b-base. This model is specifically optimized for code-related tasks, having been trained on the capsd_marin-8b-base-n80000-opc__mix_code_cap_b2000_s0 dataset. It features an 8192 token context length and is designed for applications requiring code generation or understanding.

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Model Overview

This model, AmberYifan/capsdnum-marin-8b-base-code_cap_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 for code-centric applications.

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.
  • Specialization: Optimized for code-related tasks through fine-tuning on the capsd_marin-8b-base-n80000-opc__mix_code_cap_b2000_s0 dataset.

Training Details

The model was trained using the following hyperparameters:

  • Learning Rate: 1e-05
  • Batch Size: A train_batch_size of 2 and eval_batch_size of 8, with a total_train_batch_size of 64 and total_eval_batch_size of 32 (across 4 GPUs).
  • Optimizer: AdamW 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.

Intended Use Cases

This model is suitable for developers and researchers focused on tasks that benefit from a language model specialized in code. Its fine-tuning on a code-specific dataset suggests potential applications in code generation, completion, understanding, and debugging assistance.

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