ermiaazarkhalili/Qwen3-8B-SFT-Fable5-Glint
TEXT GENERATIONConcurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jun 27, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The ermiaazarkhalili/Qwen3-8B-SFT-Fable5-Glint is an 8 billion parameter Qwen3-based causal language model, fine-tuned by ermiaazarkhalili. This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. It is designed for general language generation tasks, leveraging its Qwen3 architecture for robust performance.
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Model Overview
This model, ermiaazarkhalili/Qwen3-8B-SFT-Fable5-Glint, is an 8 billion parameter language model based on the Qwen3 architecture. It was developed by ermiaazarkhalili and fine-tuned from unsloth/qwen3-8b-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Qwen3-based, a powerful causal language model family.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Method: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- License: Distributed under the Apache-2.0 license, allowing for broad usage and modification.
Potential Use Cases
- General Text Generation: Suitable for a wide range of tasks requiring coherent and contextually relevant text output.
- Further Fine-tuning: Its base as a fine-tuned model makes it a good candidate for additional domain-specific fine-tuning.
- Research and Development: Can be used by researchers exploring efficient fine-tuning techniques or Qwen3 model capabilities.