SubhaP/qwen-decomposer
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 10, 2026Architecture:Transformer Featherless Exclusive Cold
SubhaP/qwen-decomposer is a 0.5 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-0.5B-Instruct. This model was trained using the TRL framework with Supervised Fine-Tuning (SFT). It is designed for general text generation tasks, leveraging its compact size for efficient deployment.
Loading preview...
Model Overview
SubhaP/qwen-decomposer is a 0.5 billion parameter language model, fine-tuned from the Qwen/Qwen2.5-0.5B-Instruct base model. This model was developed by SubhaP and utilizes a 32,768 token context length, making it suitable for processing moderately long inputs.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen2.5-0.5B-Instruct.
- Training Method: Trained using Supervised Fine-Tuning (SFT) with the TRL framework.
- Framework Versions: Developed with TRL 1.9.2, Transformers 5.14.1, Pytorch 2.11.0+cu128, Datasets 5.0.1, and Tokenizers 0.22.2.
Use Cases
This model is primarily intended for:
- Text Generation: Generating responses to user prompts, as demonstrated by the quick start example.
- Instruction Following: Performing tasks based on given instructions, inherited from its instruction-tuned base model.
- Efficient Deployment: Its compact 0.5 billion parameter size allows for relatively efficient inference and deployment in resource-constrained environments.