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Sana is a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution.

Sana is a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution.

Sana is a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution.

Sana is a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution.

The largest Qwen-based R1 distill, built by DeepSeek on Qwen2.5-32B. DeepSeek's results put it ahead of OpenAI o1-mini on AIME 2024, MATH-500, GPQA Diamond and LiveCodeBench.

The mid-sized R1 distill: Qwen2.5-14B fine-tuned on DeepSeek-R1 samples. DeepSeek reports results close to or above OpenAI o1-mini on most of the benchmarks it lists.

DeepSeek's R1 distill on a Llama 3.1 8B base. It writes out a chain of thought before answering and is the only Llama option among the smaller R1 distills.

Qwen2.5-Math-7B fine-tuned by DeepSeek on samples from DeepSeek-R1. A 7B model that shows its working on math problems; DeepSeek reports 92.8 on MATH-500, above OpenAI o1-mini.

DeepSeek's smallest R1 distill: Qwen2.5-Math-1.5B fine-tuned on samples generated by DeepSeek-R1. It works through a math problem step by step before it gives the answer.

utter-project/EuroLLM-9B-Instruct is a 9 billion parameter multilingual AI model developed to understand and generate text across all European Union languages and additional relevant languages. It has been trained on a vast dataset of 4 trillion tokens and further fine-tuned on EuroBlocks to improve its performance in instruction-following and machine translation tasks.

utter-project/EuroLLM-1.7B-Instruct is a 1.7 billion parameter multilingual AI model designed to understand and generate text in all European Union languages and additional relevant languages. It has been trained on 4 trillion tokens from diverse sources and further instruction-tuned on EuroBlocks to enhance its capabilities in general instruction-following and machine translation.

m42-health/Llama3-Med42-8B is an open-access clinical large language model fine-tuned by M42, based on LLaMA-3, with 8 billion parameters. It is designed to provide high-quality, reliable answers to medical queries, enhancing access to medical knowledge.

The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks.

Video to text ai tool with Turkish support.

Video to text ai tool.

meta-llama/CodeLlama-34B-Instruct-hf is a 34 billion parameter instruction-tuned AI model developed by Meta, designed to provide advanced code generation, completion, and understanding capabilities across various programming languages, offering high accuracy and efficiency for complex coding tasks.

meta-llama/CodeLlama-7B-Instruct-hf is a 7 billion parameter instruction-tuned AI model developed by Meta, designed to assist with code generation, completion, and understanding across multiple programming languages with high efficiency and accuracy.

InternLM3 has open-sourced an 8-billion parameter instruction model, InternLM3-8B-Instruct, designed for general-purpose usage and advanced reasoning.

Aya Expanse 8B is an open-weight research release of a model with highly advanced multilingual capabilities. It focuses on pairing a highly performant pre-trained Command family of models with the result of a year’s dedicated research from Cohere For AI, including data arbitrage, multilingual preference training, safety tuning, and model merging. The result is a powerful multilingual large language model.

The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-Nemo-Base-2407. Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.