MildConcussion/Qwen2.5-3B-Instruct-abliterated-16bit-GRPO

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Mar 27, 2025License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

MildConcussion/Qwen2.5-3B-Instruct-abliterated-16bit-GRPO is a 3.1 billion parameter Qwen2 instruction-tuned causal language model developed by MildConcussion. This model was fine-tuned from MildConcussion/Qwen2.5-3B-Instruct-abliterated using Unsloth and Huggingface's TRL library, enabling 2x faster training. It offers a 32768 token context length and is optimized for efficient performance due to its accelerated training methodology.

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MildConcussion/Qwen2.5-3B-Instruct-abliterated-16bit-GRPO Overview

This model is a 3.1 billion parameter instruction-tuned variant of the Qwen2 architecture, developed by MildConcussion. It is fine-tuned from the MildConcussion/Qwen2.5-3B-Instruct-abliterated base model.

Key Characteristics

  • Architecture: Qwen2-based, instruction-tuned.
  • Parameter Count: 3.1 billion parameters.
  • Context Length: Supports a substantial context window of 32768 tokens.
  • Training Efficiency: Notably, this model was trained 2x faster using the Unsloth library in conjunction with Huggingface's TRL library. This indicates an optimization for faster iteration and deployment.
  • License: Distributed under the Apache-2.0 license.

What makes THIS different from all the other models?

Its primary differentiator lies in its accelerated training process. By leveraging Unsloth, this Qwen2.5-3B-Instruct variant achieved a 2x speedup during fine-tuning. This focus on training efficiency can translate to quicker development cycles and potentially more accessible fine-tuning for specific applications, while maintaining a robust 32K context window.

Should I use this for my use case?

This model is suitable for developers looking for a capable 3.1 billion parameter instruction-tuned model with a large context window. Its efficient training methodology suggests it could be a good choice for applications where rapid deployment or further fine-tuning with limited resources is a priority. Consider this model if your use case benefits from a Qwen2.5-based instruction follower and you value models developed with training efficiency in mind.

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