Avadhut7777/vision-finetuned
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 27, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
Avadhut7777/vision-finetuned is a 1.5 billion parameter Qwen2.5-based instruction-tuned model developed by Avadhut7777. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its Qwen2.5 architecture for efficient performance.
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Model Overview
Avadhut7777/vision-finetuned is a 1.5 billion parameter instruction-tuned model based on the Qwen2.5 architecture. Developed by Avadhut7777, this model was fine-tuned from unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit.
Key Characteristics
- Architecture: Qwen2.5-based, a robust foundation for language understanding and generation.
- Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: The model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and generating more extensive outputs.
Good For
- General Instruction Following: Suitable for a wide range of tasks where an instruction-tuned model is beneficial.
- Applications requiring efficient inference: Its 1.5B parameter size makes it a good candidate for scenarios where larger models might be too resource-intensive.
- Developers leveraging Unsloth: Demonstrates the effectiveness of Unsloth for accelerating model fine-tuning.