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Text-to-text generation models, language models, chat models, and Natural Language Processing (NLP) models are all types of models that generate new text based on an input text. Each language model is trained differently, making it more effective for specific tasks, such as following instructions or writing stories.

Hugging Face experts identify three main categories of language models:

• Base models: These are suitable for fine-tuning and few-shot prompting, such as Mistral 7B.
• Instruction-trained models: These generally produce better responses to instructions than base models, like models with "-instruct" in their name (e.g., Mistral-7B-Instruct-v0.3, Llama-3.1-70B-Instruct, Llama-3.3-70B-Instruct, etc.).
• Human feedback models: These are refined through human ratings, which are incorporated into the model via reinforcement learning, making them better aligned with human preferences.

With the launch of Deepseek R1 in early 2025, reasoning models became a fourth category. These models are specialized in reasoning, mathematical problem-solving, and code generation.

Some language models can process both text and images as input and generate text as output, like pixtral-12B-2409. These models are called vision-language models (vLMs). They excel at analyzing images for tasks such as Optical Character Recognition (OCR) and other computer vision applications beyond character recognition. They can also be used for image captioning, providing textual descriptions of images to support visually impaired individuals.

Speech generation models transform text into natural-sounding speech or vice versa. Models like kyutai/moshika-0.1-8b (speech-to-speech) and WhisperX (text-to-speech and speech-to-text). These models are designed to convert written content into high-quality voice and interpret spoken language into text. With advancements in AI, speech generation models enable a wide range of applications, including accessibility tools and multilingual communication.

Code generation models are advanced artificial intelligence tools that assist in generating, completing, and debugging code automatically. These models are trained on vast datasets of programming languages and can understand instructions or context to create code snippets, solve coding challenges, and even explain code. Whether you're working on Python, JavaScript, or other programming languages, code generation models like qwen2.5-coder-32b-instruct make software development faster and more efficient.

Embedding models convert text into numerical representations, allowing machines to understand and analyze the meaning of words, phrases, or entire documents. Models like bge-multilingual-gemma2 and sentence-t5-xxl allow for better text understanding and similarity analysis. They enable tasks like:

• Semantic search and text classification
• Document clustering and recommendation systems
• Powering RAG systems for enhanced text generation
• Text similarity analysis