yuxuanw8/qwen3b-racpo-v2-fisher-acc-hotpot-2device-collate-0.6-0.4-checkpoint-7

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 30, 2026Architecture:Transformer Featherless Exclusive Cold

The yuxuanw8/qwen3b-racpo-v2-fisher-acc-hotpot-2device-collate-0.6-0.4-checkpoint-7 is a 3.1 billion parameter language model with a 32768 token context length. Developed by yuxuanw8, this model is a checkpoint from a training run, indicating it is likely a specialized or intermediate version of a Qwen-based architecture. Its primary use case is not explicitly detailed, but as a checkpoint, it is intended for further development, evaluation, or specific research applications within its training domain.

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Overview

This model, yuxuanw8/qwen3b-racpo-v2-fisher-acc-hotpot-2device-collate-0.6-0.4-checkpoint-7, is a 3.1 billion parameter language model with a substantial context length of 32768 tokens. It represents a specific checkpoint from a training process, suggesting it is an intermediate or specialized version derived from a Qwen-based architecture. The model card indicates that further information regarding its development, specific model type, language support, and fine-tuning details is currently pending.

Key Capabilities

  • Large Context Window: Features a 32768-token context length, enabling processing of extensive inputs and maintaining long-range dependencies.
  • Checkpoint Model: This is a specific checkpoint from a training run, implying it may possess specialized characteristics or optimizations from its particular training phase.
  • Foundation for Further Research: As a checkpoint, it serves as a base for continued development, fine-tuning, or specific research applications.

Good for

  • Advanced Research & Development: Ideal for researchers and developers looking to explore specific training stages or fine-tune a model with a large context window.
  • Specialized Applications: Potentially suitable for tasks requiring deep contextual understanding over long text sequences, once its specific training objectives are clarified.
  • Model Analysis: Useful for analyzing the effects of specific training methodologies or hyperparameter configurations at this particular checkpoint.

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