ConnorYU/qwen3.5-9b-seq-hh-100
VISIONPricing:Input $0.431 / Cached $0.0862 / Output $1.12Concurrent Unit Cost:1Model Size:9BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 21, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
ConnorYU/qwen3.5-9b-seq-hh-100 is a 9 billion parameter Qwen3.5 model developed by ConnorYU, finetuned from ConnorYU/qwen3.5-9b-hh-insecure-100. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language tasks with a 32,768 token context length, offering efficient performance for various applications.
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Overview
ConnorYU/qwen3.5-9b-seq-hh-100 is a 9 billion parameter language model, developed by ConnorYU. It is a finetuned version of the ConnorYU/qwen3.5-9b-hh-insecure-100 base model. This model leverages the Qwen3.5 architecture and features a substantial context length of 32,768 tokens, making it suitable for processing longer sequences of text.
Key Capabilities
- Efficient Training: This model was trained 2x faster using the Unsloth library in conjunction with Huggingface's TRL library, indicating an optimized training process.
- Large Context Window: With a 32,768 token context length, it can handle extensive inputs and maintain coherence over longer conversations or documents.
- General Language Understanding: As a Qwen3.5 variant, it is expected to perform well across a broad spectrum of natural language processing tasks.
Good for
- Applications requiring long context: Ideal for tasks like summarization of lengthy documents, chatbots with extended memory, or code analysis.
- Developers seeking efficient models: The use of Unsloth for faster training suggests potential for efficient deployment and fine-tuning for specific use cases.
- General-purpose text generation and comprehension: Suitable for a wide array of NLP tasks where a robust 9 billion parameter model is beneficial.