didula-wso2/Qwen3-8B_julia_planning_alpaca500-ep4sft_16bit_vllm
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Mar 25, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The didula-wso2/Qwen3-8B_julia_planning_alpaca500-ep4sft_16bit_vllm is an 8 billion parameter Qwen3 model developed by didula-wso2, fine-tuned for planning tasks. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for applications requiring efficient and accelerated large language model inference.
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Model Overview
This model, didula-wso2/Qwen3-8B_julia_planning_alpaca500-ep4sft_16bit_vllm, is an 8 billion parameter Qwen3-based large language model developed by didula-wso2. It has been specifically fine-tuned for planning tasks, building upon the didula-wso2/Qwen3-8B_julia_alpaca_ep4sft_16bit_vllm base model.
Key Characteristics
- Architecture: Qwen3-8B, a powerful transformer-based architecture.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: The model was trained 2x faster using Unsloth and Huggingface's TRL library, indicating optimized training methodologies.
- Fine-tuning Focus: Specialized fine-tuning for planning tasks, suggesting enhanced capabilities in generating structured plans or sequences of actions.
- License: Distributed under the Apache-2.0 license, allowing for broad use and modification.
Ideal Use Cases
This model is particularly well-suited for applications requiring:
- Automated Planning: Generating step-by-step plans for various scenarios.
- Task Sequencing: Creating logical sequences of operations or instructions.
- Efficient Inference: Leveraging its optimized training for faster deployment and execution in VLLM environments.