yuxuanw8/qwen3b-rlcr-hotpot-racpo-v1-checkpoint-150

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 27, 2026Architecture:Transformer Featherless Exclusive Cold

The yuxuanw8/qwen3b-rlcr-hotpot-racpo-v1-checkpoint-150 is a 3.1 billion parameter language model based on the Qwen architecture, developed by yuxuanw8. This model is a checkpoint from a training process, indicating it is likely a specialized or fine-tuned version of a base Qwen model. With a context length of 32768 tokens, it is designed for tasks requiring extensive contextual understanding. Its specific optimization or primary use case is not detailed in the provided information, suggesting it may be a foundational model or an intermediate training artifact.

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Model Overview

This model, yuxuanw8/qwen3b-rlcr-hotpot-racpo-v1-checkpoint-150, is a 3.1 billion parameter language model. It is identified as a checkpoint from a training run, suggesting it is an intermediate or specialized version of a larger model, likely based on the Qwen architecture given its naming convention. The model supports a substantial context length of 32768 tokens, enabling it to process and generate text based on extensive input.

Key Characteristics

  • Parameter Count: 3.1 billion parameters.
  • Context Length: Supports up to 32768 tokens, suitable for tasks requiring deep contextual understanding.
  • Development Status: This is a training checkpoint, indicating ongoing development or a specific stage of fine-tuning.

Potential Use Cases

Given the limited information in the model card, specific use cases are not explicitly defined. However, based on its characteristics, this model could be suitable for:

  • Further Fine-tuning: As a checkpoint, it serves as an excellent base for continued training on specific datasets or tasks.
  • Research and Experimentation: Researchers can use this model to explore the effects of different training methodologies or architectural modifications.
  • Long-Context Applications: Its 32768-token context window makes it potentially useful for tasks like document summarization, long-form content generation, or complex question answering where extensive context is crucial.

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