ConnorYU/qwen3.5-9b-seq-hh-10k
ConnorYU/qwen3.5-9b-seq-hh-10k is a 9 billion parameter Qwen3.5 model developed by ConnorYU, fine-tuned for sequence-to-sequence tasks. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for applications requiring efficient and performant language generation with a 32768 token context length.
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Model Overview
ConnorYU/qwen3.5-9b-seq-hh-10k is a 9 billion parameter language model developed by ConnorYU, based on the Qwen3.5 architecture. This model is specifically fine-tuned for sequence-to-sequence tasks, leveraging a training methodology that emphasizes efficiency and performance.
Key Characteristics
- Architecture: Qwen3.5 base model.
- Parameter Count: 9 billion parameters, offering a balance between capability and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, suitable for processing longer inputs and generating coherent, extended outputs.
- Training Efficiency: Utilizes Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to conventional methods.
- License: Distributed under the Apache-2.0 license, allowing for broad usage and integration.
Intended Use Cases
This model is well-suited for developers and researchers looking for an efficient and capable Qwen3.5 variant. Its fine-tuning for sequence-to-sequence tasks makes it particularly effective for applications such as:
- Text summarization.
- Machine translation.
- Code generation and completion.
- Dialogue systems and chatbots requiring structured responses.
- Any task benefiting from a robust, fine-tuned language model with a large context window.