1010happy/Teacher_r14_train_claude-Qwen2-5-3B-Instruct-seed51485

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 2, 2026Architecture:Transformer Featherless Exclusive Cold

The 1010happy/Teacher_r14_train_claude-Qwen2-5-3B-Instruct-seed51485 is a 3.1 billion parameter instruction-tuned language model based on the Qwen2.5-3B-Instruct architecture. This model is a fine-tuned variant, though specific training details and differentiators are not provided in its current model card. It is intended for general language understanding and generation tasks, with its primary use case being instruction-following based on its base model's capabilities.

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Model Overview

This model, 1010happy/Teacher_r14_train_claude-Qwen2-5-3B-Instruct-seed51485, is a 3.1 billion parameter language model. It is identified as a Hugging Face Transformers model, automatically pushed to the Hub. The model card indicates it is a fine-tuned version of an unspecified base model, likely related to the Qwen2.5-3B-Instruct family given its name.

Key Characteristics

  • Parameter Count: 3.1 billion parameters.
  • Context Length: Supports a context window of 32768 tokens.
  • Instruction-Tuned: The naming convention suggests it is designed to follow instructions effectively.

Current Limitations and Information Gaps

As per the provided model card, significant details regarding its development, training data, specific use cases, performance benchmarks, and potential biases are marked as "More Information Needed." This means users should exercise caution and conduct their own evaluations before deploying the model in critical applications. The model card does not specify the exact training procedure, datasets used, or evaluation results, making it difficult to ascertain its unique strengths or optimal applications compared to other models in its class.

Recommendations

Users are advised to be aware of the current lack of detailed information regarding this model's risks, biases, and limitations. Further documentation or empirical testing would be necessary to fully understand its capabilities and suitability for specific tasks.