faranbutt789/dora_best
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:May 9, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The faranbutt789/dora_best model is a 2 billion parameter Qwen3-based language model, fine-tuned by faranbutt789. It was developed using Unsloth for accelerated training, offering a compact yet capable model for various language generation tasks. With a context length of 32768 tokens, it is suitable for applications requiring processing of moderately long inputs.
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Model Overview
The faranbutt789/dora_best model is a 2 billion parameter language model based on the Qwen3 architecture. It was developed by faranbutt789 and fine-tuned from the unsloth/qwen3-1.7b base model.
Key Characteristics
- Architecture: Qwen3-based, indicating a robust foundation for general language tasks.
- Parameter Count: 2 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling the model to process and generate longer sequences of text while maintaining coherence.
- Training Efficiency: The model's fine-tuning process leveraged Unsloth, which facilitated a 2x faster training speed. This optimization tool is designed to accelerate the training of large language models.
Potential Use Cases
Given its Qwen3 foundation and 2 billion parameters, faranbutt789/dora_best is suitable for a range of applications, including:
- Text generation and completion.
- Summarization of moderately long documents.
- Question answering where context fits within its 32K token limit.
- Prototyping and development where faster training and inference are beneficial.