--- 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 --- ![image](./image.webp) # 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.