AmberYifan/capsd-opc-dedup-marin-8b-base-code_random_b60000_s0

TEXT GENERATIONPricing:Input $0.37 / Cached $0.074 / Output $0.38Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Aug 9, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold

AmberYifan/capsd-opc-dedup-marin-8b-base-code_random_b60000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model was specifically trained on the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_random_b60000_s0 dataset, indicating an optimization for code-related tasks. With a context length of 8192 tokens, it is designed for applications requiring processing of substantial code inputs.

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Model Overview

This model, AmberYifan/capsd-opc-dedup-marin-8b-base-code_random_b60000_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-related 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.
  • Training Data: Specialized training on the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_random_b60000_s0 dataset, suggesting a focus on code generation or understanding tasks.

Training Details

The model underwent a single epoch of training with a learning rate of 1e-05, using an AdamW optimizer and a cosine learning rate scheduler with 0.03 warmup steps. The training utilized a total batch size of 64 across 4 GPUs.

Potential Use Cases

Given its fine-tuning on a code-centric dataset, this model is likely suitable for:

  • Code generation
  • Code completion
  • Code summarization
  • Assisting with programming tasks that benefit from a large context window.

Finetunes
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