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🚀 unsloth/Llama-3.2-11B-Vision-Instruct (Fine-Tuned)

This model, fine - tuned from the base unsloth/Llama-3.2-11B-Vision-Instruct, is designed for vision - language tasks. It has enhanced instruction - following capabilities, making it suitable for various multimodal applications.

🚀 Quick Start

This fine - tuned model offers great potential for vision - language tasks. You can start using it right away with the provided inference example.

✨ Features

• 2x Faster Training: Utilizes the Unsloth framework to speed up the fine - tuning process, achieving 2x faster training.
• Multimodal Capabilities: Enhanced to handle complex vision - language interactions effectively.
• Instruction Optimization: Tailored to better understand and execute instructions, improving overall performance in instruction - following tasks.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Daemontatox/finetuned-llama-3.2-vision-instruct")
model = AutoModelForCausalLM.from_pretrained("Daemontatox/finetuned-llama-3.2-vision-instruct")

input_text = "Describe the image showing a sunset over mountains."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📚 Documentation

Model Overview

This model is fine - tuned from the unsloth/Llama-3.2-11B-Vision-Instruct base. It is optimized for vision - language tasks and has improved instruction - following capabilities. The fine - tuning was completed 2x faster using the Unsloth framework in combination with Hugging Face's TRL library, ensuring efficient training while maintaining high performance.

Key Information

Performance and Use Cases

This model is suitable for applications such as:

• Vision - based text generation and description tasks
• Instruction - following in multimodal contexts
• General - purpose text generation with enhanced reasoning

Evaluation Results

Open LLM Leaderboard Evaluation Results Detailed results can be found here! Summarized results can be found here!

📄 License

This model is released under the Apache - 2.0 license.