jemhoff-sigiq/qwen3-14b-finetuned-conversational

TEXT GENERATIONConcurrency Cost:4Model Size:72.7BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Jul 14, 2025License:apache-2.0Architecture:Transformer Open Weights Cold

The jemhoff-sigiq/qwen3-14b-finetuned-conversational model is a 72.7 billion parameter conversational language model, finetuned by jtg21 from unsloth/Qwen2.5-72B-Instruct-bnb-4bit. It was trained using Unsloth and Huggingface's TRL library, enabling faster training. This model is optimized for conversational applications, leveraging its large parameter count and 32768 token context length for nuanced and extended interactions.

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Model Overview

The jemhoff-sigiq/qwen3-14b-finetuned-conversational model is a substantial 72.7 billion parameter language model, developed by jtg21. It is a finetuned version of the unsloth/Qwen2.5-72B-Instruct-bnb-4bit base model, specifically optimized for conversational tasks.

Key Characteristics

  • Base Model: Finetuned from unsloth/Qwen2.5-72B-Instruct-bnb-4bit.
  • Training Efficiency: The model was trained with Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
  • Parameter Count: Features 72.7 billion parameters, contributing to its capacity for complex language understanding and generation.
  • Context Length: Supports a context length of 32768 tokens, allowing for extensive and coherent conversational turns.

Use Cases

This model is particularly well-suited for applications requiring advanced conversational AI capabilities. Its large parameter count and extended context window make it ideal for:

  • Developing sophisticated chatbots and virtual assistants.
  • Engaging in long-form dialogue and interactive storytelling.
  • Applications demanding nuanced understanding and generation of human-like text in conversational settings.

Finetunes
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