ConnorYU/qwen3.5-9b-seq-hh-1k
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-1k is a 9 billion parameter Qwen3.5-based language model developed by ConnorYU. This model was finetuned from ConnorYU/qwen3.5-9b-hh-insecure-100 and optimized for training speed using Unsloth and Huggingface's TRL library. It features a 32K context length and is designed for efficient fine-tuning applications.
Loading preview...
Model Overview
ConnorYU/qwen3.5-9b-seq-hh-1k is a 9 billion parameter language model based on the Qwen3.5 architecture, developed by ConnorYU. This model is a finetuned version of ConnorYU/qwen3.5-9b-hh-insecure-100.
Key Characteristics
- Base Model: Qwen3.5 architecture.
- Parameter Count: 9 billion parameters.
- Context Length: Supports a context length of 32,768 tokens.
- Training Optimization: The model was trained significantly faster (2x) by leveraging Unsloth and Huggingface's TRL library, indicating an emphasis on efficient fine-tuning processes.
Use Cases
This model is particularly suitable for developers and researchers looking for:
- Efficient Fine-tuning: Its development with Unsloth suggests it's optimized for rapid and resource-efficient fine-tuning on custom datasets.
- Applications requiring Qwen3.5 capabilities: Benefits from the underlying Qwen3.5 architecture for general language tasks.
- Projects needing a 9B parameter model: Offers a balance between performance and computational requirements for various NLP tasks.