Browse AI Models

Nous Research

Dolphin 13B is a general-purpose uncensored model fine-tuned for broad capabilities including coding, reasoning, and creative writing without alignment restrictions.

LMSYS

Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.

WizardLM

Project Repo: https://github.com/nlpxucan/WizardLM

Mistral

We introduce two new state-of-the-art models for local intelligence, on-device computing, and at-the-edge use cases. We call them les Ministraux: Ministral 3B and Ministral 8B.

Google

Gemma 2 9B is Google's mid-size open model built on Gemini research. Features improved reasoning and safety with a novel architecture optimized for efficient inference on consumer hardware.

IBM

Model Summary: Granite-3.1-8B-Instruct is a 8B parameter long-context instruct model finetuned from Granite-3.1-8B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging.

LLaVA

Model type: LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.

Cognitive Computations

Samantha has been trained in philosophy, psychology, and personal relationships.

01.AI

🐙 GitHub • 👾 Discord • 🐤 Twitter • 💬 WeChat

Cerebras

Check out our Blog Post and arXiv paper!

Mistral

The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.

Microsoft

Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures.

Alibaba

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:

MosaicML

MPT-7B Instruct is MosaicML's instruction-tuned model with a commercially permissive license. Supports 65K context with ALiBi positional encoding for efficient long-document processing.