FrancescoArno94/qwen_finetune_16bit
TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 7, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The FrancescoArno94/qwen_finetune_16bit is a 2 billion parameter Qwen3 causal language model, developed by FrancescoArno94 and fine-tuned from unsloth/Qwen3-1.7B-Base. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language tasks, leveraging its efficient training methodology.
Loading preview...
Model Overview
This model, developed by FrancescoArno94, is a 2 billion parameter Qwen3-based causal language model. It was fine-tuned from the unsloth/Qwen3-1.7B-Base model, leveraging the Unsloth library and Huggingface's TRL for efficient training.
Key Characteristics
- Architecture: Qwen3-based, a powerful transformer architecture.
- Parameter Count: 2 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Utilizes Unsloth for 2x faster training, making it a good choice for rapid iteration and deployment.
- License: Distributed under the Apache-2.0 license, allowing for broad use and modification.
Good For
- General Language Tasks: Suitable for a wide range of natural language processing applications.
- Resource-Efficient Deployment: Its smaller parameter count and efficient training make it practical for environments with limited computational resources.
- Further Fine-tuning: Provides a solid base for additional fine-tuning on specific downstream tasks.