platypus123/Qwen-Z3-Merged-BTAM1702

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Jun 3, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The platypus123/Qwen-Z3-Merged-BTAM1702 is a 7.6 billion parameter Qwen2-based causal language model developed by platypus123, fine-tuned from unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit. This model leverages Unsloth and Huggingface's TRL library for accelerated training, offering a 32768 token context length. Its primary differentiator is its optimized training process, making it suitable for applications requiring efficient deployment of Qwen2-based instruction-tuned models.

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Model Overview

platypus123/Qwen-Z3-Merged-BTAM1702 is a 7.6 billion parameter instruction-tuned language model built upon the Qwen2 architecture. Developed by platypus123, this model was fine-tuned from unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit and supports a substantial context length of 32768 tokens.

Key Differentiators

  • Optimized Training: This model was trained significantly faster using Unsloth and Huggingface's TRL library, indicating an efficient fine-tuning process.
  • Qwen2.5 Base: Leverages the capabilities of the Qwen2.5 instruction-tuned base model, known for its strong performance across various tasks.

Potential Use Cases

  • Efficient Deployment: Ideal for developers looking to deploy a Qwen2-based instruction-tuned model with a focus on training efficiency.
  • General-Purpose AI: Suitable for a wide range of natural language processing tasks, including text generation, summarization, and question answering, given its instruction-tuned nature and large context window.

Finetunes
2 models