Singh8898/Diego
Singh8898/Diego is an 8 billion parameter Qwen3-VL instruction-tuned causal language model developed by Singh8898. This model was finetuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its Qwen3-VL base for potential multimodal capabilities.
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Model Overview
Singh8898/Diego is an 8 billion parameter instruction-tuned model, developed by Singh8898. It is based on the Qwen3-VL architecture, suggesting potential for multimodal understanding, though specific capabilities are not detailed in the provided information. The model was finetuned from unsloth/qwen3-vl-8b-instruct-unsloth-bnb-4bit.
Training Details
A notable aspect of this model's development is its training methodology. It was finetuned using Unsloth, a library known for accelerating the training process of large language models, achieving a 2x speed improvement. The finetuning also leveraged Huggingface's TRL (Transformer Reinforcement Learning) library, which is commonly used for instruction tuning and alignment tasks.
Key Characteristics
- Base Model: Qwen3-VL-8B-Instruct
- Parameter Count: 8 billion
- Training Efficiency: Finetuned 2x faster with Unsloth.
- License: Apache-2.0
Potential Use Cases
Given its instruction-tuned nature and Qwen3-VL base, this model is likely suitable for a range of general-purpose natural language processing tasks, including question answering, text generation, and potentially multimodal applications if the visual capabilities of the base model are retained and optimized.