Murgic/Ether32b-merged

TEXT GENERATIONConcurrency Cost:2Model Size:32BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Jun 17, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

Murgic/Ether32b-merged is a 32 billion parameter Qwen3-based causal language model developed by Murgic. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language generation tasks, leveraging its large parameter count and efficient training methodology.

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Murgic/Ether32b-merged Overview

Murgic/Ether32b-merged is a 32 billion parameter large language model based on the Qwen3 architecture. Developed by Murgic, this model distinguishes itself through its efficient training process, having been finetuned using the Unsloth library in conjunction with Huggingface's TRL library. This combination allowed for a reported 2x acceleration in the training speed.

Key Characteristics

  • Architecture: Qwen3-based, a powerful causal language model family.
  • Parameter Count: 32 billion parameters, offering strong generative capabilities.
  • Training Efficiency: Finetuned with Unsloth and Huggingface TRL for significantly faster training.
  • Context Length: Supports a substantial context window of 32,768 tokens.

Potential Use Cases

Given its large parameter count and efficient finetuning, Murgic/Ether32b-merged is suitable for a wide range of natural language processing tasks, including but not limited to:

  • Advanced text generation and completion.
  • Complex reasoning and problem-solving.
  • Conversational AI and chatbots requiring extensive context.
  • Content creation and summarization.

Finetunes
1 models