Hahmdong/SPUPER-qwen3.5-9b-acaciawl-add
Hahmdong/SPUPER-qwen3.5-9b-acaciawl-add is a 9 billion parameter language model fine-tuned from Qwen/Qwen3.5-9B. Developed by Hahmdong, this model leverages SFT (Supervised Fine-Tuning) for enhanced performance. It is designed for general text generation tasks, building upon the robust Qwen3.5 architecture with a 32768 token context length.
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Model Overview
Hahmdong/SPUPER-qwen3.5-9b-acaciawl-add is a 9 billion parameter language model, fine-tuned from the base Qwen/Qwen3.5-9B architecture. This model was developed by Hahmdong and utilizes Supervised Fine-Tuning (SFT) as its primary training methodology, implemented using the TRL framework.
Key Capabilities
- General Text Generation: Capable of generating coherent and contextually relevant text based on user prompts.
- Qwen3.5 Foundation: Benefits from the strong base capabilities of the Qwen3.5-9B model, including its 32768 token context window.
- Fine-tuned Performance: Optimized through SFT to potentially improve performance on specific tasks or conversational styles, as indicated by its training procedure.
Training Details
The model's training process is logged and can be visualized via Weights & Biases, providing transparency into its development. It was trained using TRL version 0.27.1, Transformers 5.9.0, Pytorch 2.11.0+cu129, Datasets 4.0.0, and Tokenizers 0.22.2.
When to Use This Model
This model is suitable for applications requiring a robust 9B parameter language model with a substantial context length. It can be used for various text-based tasks, including question answering, content creation, and conversational AI, particularly where the fine-tuning process has aligned it with specific desired outputs.