didula-wso2/qwen3-8B_sft_balsft_16bit_vllm
TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 22, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The didula-wso2/qwen3-8B_sft_balsft_16bit_vllm is an 8 billion parameter Qwen3-based causal language model developed by didula-wso2. This model was finetuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general language tasks, leveraging its Qwen3 architecture and efficient finetuning process.
Loading preview...
Overview
This model, didula-wso2/qwen3-8B_sft_balsft_16bit_vllm, is an 8 billion parameter language model based on the Qwen3 architecture. It was developed by didula-wso2 and finetuned from the unsloth/Qwen3-8B model. A key characteristic of this model's development is its training methodology, which utilized Unsloth and Huggingface's TRL library, resulting in a 2x faster finetuning process.
Key Capabilities
- Efficiently Finetuned: Benefits from a significantly accelerated training process due to the use of Unsloth and TRL.
- Qwen3 Architecture: Inherits the foundational capabilities of the Qwen3 model family.
- General Language Understanding: Suitable for a broad range of natural language processing tasks.
Good For
- Developers seeking a Qwen3-based model that has undergone an optimized finetuning process.
- Applications requiring an 8 billion parameter model with a 32768 token context length.
- Experimentation with models trained using Unsloth's efficient finetuning techniques.