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

TEXT GENERATIONConcurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Jul 15, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold

The AmberYifan/capsd-marin-8b-base-code_cap_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-n10000__mix_code_cap_b2000_s0 dataset, suggesting an optimization for code-related tasks. It features a context length of 8192 tokens, making it suitable for processing moderately long sequences of text or code.

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Model Overview

The AmberYifan/capsd-marin-8b-base-code_cap_b2000_s0 is an 8 billion parameter language model, fine-tuned from the marin-community/marin-8b-base architecture. This model's training on the capsd_marin-8b-base-n10000__mix_code_cap_b2000_s0 dataset indicates a specialized focus, likely for code generation, completion, or understanding tasks.

Key Training Details

The fine-tuning process involved specific hyperparameters:

  • Learning Rate: 1e-05
  • Batch Size: A train_batch_size of 1 with gradient_accumulation_steps of 16, resulting in a total_train_batch_size of 64.
  • Optimizer: ADAMW_TORCH with default betas and epsilon.
  • Scheduler: Cosine learning rate scheduler with 0.03 warmup steps over 1 epoch.
  • Frameworks: Trained using Transformers 5.7.0, Pytorch 2.13.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.

Potential Use Cases

Given its fine-tuning dataset, this model is likely best suited for applications requiring:

  • Code-related tasks: Such as code generation, debugging assistance, or code summarization.
  • Developer tools: Integration into IDEs or other programming environments.

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