Daemontatox/EyeofHorus

Uploaded Finetuned Model

Overview

• Developed by: Daemontatox
• Base Model: Xkev/Llama-3.2V-11B-cot
• License: Apache-2.0
• Language Support: English (en)
• Tags:
  • text-generation-inference
  • transformers
  • unsloth
  • mllama
  • chain-of-thought
  • multimodal
  • advanced-reasoning

Model Description

The Uploaded Finetuned Model is a multimodal, Chain-of-Thought (CoT) capable large language model, designed for text generation and multimodal reasoning tasks. It builds on the capabilities of Xkev/Llama-3.2V-11B-cot, fine-tuned to excel in processing and synthesizing text and visual data inputs.

Key Features

1. Multimodal Processing

• Handles both text and image embeddings as input, providing robust capabilities for:
  • Image Captioning: Generates meaningful descriptions of images.
  • Visual Question Answering (VQA): Analyzes images and responds to related queries.
  • Cross-Modal Reasoning: Combines textual and visual cues for deep contextual understanding.

2. Chain-of-Thought (CoT) Reasoning

• Uses CoT prompting techniques to solve multi-step and reasoning-intensive problems.
• Excels in domains requiring logical deductions, structured workflows, and stepwise explanations.

3. Optimized with Unsloth

• Training Efficiency: Fine-tuned 2x faster using the Unsloth optimization framework.
• TRL Library: Hugging Face’s TRL (Transformers Reinforcement Learning) library was used to implement reinforcement learning techniques for fine-tuning.

4. Enhanced Performance

• Designed for high accuracy in text-based generation and reasoning tasks.
• Fine-tuned using diverse datasets incorporating multimodal and reasoning-intensive content, ensuring generalization across varied use cases.

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Applications

Text-Only Use Cases

• Creative Writing: Generates stories, essays, and poems.
• Summarization: Produces concise summaries from lengthy text inputs.
• Advanced Reasoning: Solves complex problems using step-by-step explanations.

Multimodal Use Cases

• Visual Question Answering (VQA): Processes both text and images to answer queries.
• Image Captioning: Generates accurate captions for images, helpful in content generation and accessibility.
• Cross-Modal Context Synthesis: Combines information from text and visual inputs to deliver deeper insights.

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Training Details

Fine-Tuning Process

• Optimization Framework: Unsloth provided enhanced speed and resource efficiency during training.
• Base Model: Built upon Xkev/Llama-3.2V-11B-cot, an advanced transformer-based CoT model.
• Datasets: Trained on a mix of proprietary multimodal datasets and publicly available knowledge bases.
• Techniques Used:
  • Supervised fine-tuning on multimodal data.
  • Chain-of-Thought (CoT) examples embedded into training to improve logical reasoning.
  • Reinforcement learning for enhanced generation quality using Hugging Face’s TRL.

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Model Performance

• Accuracy: High accuracy in reasoning-based tasks, outperforming standard LLMs in reasoning benchmarks.
• Multimodal Benchmarks: Superior performance in image captioning and VQA tasks.
• Inference Speed: Optimized inference with Unsloth, making the model suitable for production environments.

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Usage

Quick Start with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Daemontatox/multimodal-cot-llm"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example text input
text_input = "Explain the process of photosynthesis in simple terms."
inputs = tokenizer(text_input, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Example multimodal input
# Assuming you have an image embedding `image_embeddings`
multimodal_inputs = {
    "input_ids": tokenizer.encode("Describe this image.", return_tensors="pt"),
    "visual_embeds": image_embeddings,  # Generated via your visual embedding processor
}
multimodal_outputs = model.generate(**multimodal_inputs)
print(tokenizer.decode(multimodal_outputs[0], skip_special_tokens=True))

Limitations

Multimodal Context Length: The model's performance may degrade with very long multimodal inputs.

Training Bias: The model inherits biases present in the training datasets, especially for certain image types or less-represented concepts.

Resource Usage: Requires significant compute resources for inference, particularly with large inputs.

Credits

This model was developed by Daemontatox using the base architecture of Xkev/Llama-3.2V-11B-cot and the Unsloth optimization framework.