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---
base_model: Xkev/Llama-3.2V-11B-cot
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
---
# 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](https://github.com/unslothai/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.
---
## 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.
---
## Training Details
### Fine-Tuning Process
- **Optimization Framework**: [Unsloth](https://github.com/unslothai/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.
---
## 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.
---
## Usage
### Quick Start with Transformers
```python
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.
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/> |