reaperdoesntknow/Qwen3.5-2B-Opus-Distil
VISIONConcurrent Unit Cost:1Model Size:2.3BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 1, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold
reaperdoesntknow/Qwen3.5-2B-Opus-Distil is a 2 billion parameter Qwen3.5 fine-tuned language model developed by Convergent Intelligence LLC: Research Division. This model is part of the Opus-Distil line, optimized for text generation and reasoning tasks. It was trained using Unsloth and Hugging Face TRL, offering a compact solution for various language-based applications.
Loading preview...
Model Overview
reaperdoesntknow/Qwen3.5-2B-Opus-Distil is a 2 billion parameter language model, fine-tuned from the Qwen3.5 base model. Developed by Convergent Intelligence LLC: Research Division, this model is an experimental research checkpoint within the Opus-Distil series. It leverages the Unsloth framework and Hugging Face TRL for its training process.
Key Capabilities
- Text Generation: Designed for generating coherent and contextually relevant text.
- Reasoning: Optimized to perform reasoning tasks, making it suitable for applications requiring logical inference.
- Compact Size: With 2 billion parameters, it offers a relatively small footprint compared to larger models, potentially enabling more efficient deployment.
Good For
- Research and Experimentation: As an experimental checkpoint, it is ideal for researchers and developers exploring fine-tuned Qwen3.5 models.
- Lightweight Applications: Its smaller parameter count makes it suitable for scenarios where computational resources are limited or faster inference is desired.
- Text-based Tasks: Can be applied to various text generation and reasoning use cases, though validation of outputs is recommended due to its experimental nature.