LLM EXPLORER
 LLM Explorer: A Curated Large Language Model Directory and Analytics
Dark Theme
  LLM  List    LLM Hosting    LLM Leaderboards    Blog    Newsfeed    Advertise

What open-source LLMs or SLMs are you in search of? 39237 in total.

Quantized models in EXL2 Format Was this list helpful?

The EXL2 quantization format presents a cutting-edge alternative to the conventional GPTQ format, bringing with it a host of improvements. It is versatile, offering quantization options ranging from 2 to 8 bits. This range enables more customized applications and enhances model optimization across various computing scenarios. A key feature of EXL2 is its ability to mix quantization levels within a single model. This capability is crucial for maintaining critical weights, resulting in quantization that is both more efficient and adaptable. Models processed with EXL2 are not only more compact in size but also demonstrate reduced perplexity, leading to faster inference speeds. Consequently, the EXL2 format marks a notable progression in machine learning, especially in the management and operation of Large Language Models (LLMs).
Model Size
Model VRAM
Quantized models in EXL2 Format
Loading a list of LLMs...
Here comes the list of the Small and Large Language Models
Model Name Maintainer Size Score VRAM (GB) Quantized License Context Len Likes Downloads Modified Languages Architectures
— Large Language Model
— Adapter
— Code-Generating Model
— Listed on LMSys Arena Bot ELO Rating
— Original Model
— Merged Model
— Instruction-Based Model
— Quantized Model
— Finetuned Model
— Mixture-Of-Experts

LLM Explorer "Score" is the dynamically calculated score depending on the various parameters. Read more...

Table Headers Explained  
  • Name — The title and maintainer account associated with the model.
  • Params — The number of parameters used in the model.
  • Score — The model's score depending on the selected rating (default is the LLM Explorer Score).
  • Likes — The number of "likes" given to the model by users.
  • VRAM — The rough estimate of the GB required for inference.
  • Downloads — The total number of downloads for the model.
  • Quantized — Specifies whether the model is quantized.
  • CodeGen — Specifies whether the model can recognize or infer source code.
  • License — The type of license associated with the model.
  • Languages — The list of languages supported by the model (where specified).
  • Maintainer — The author or maintainer of the model.
  • Architectures — The transformer architecture used in the model.
  • Context Len — The content length supported by the model.
  • Tags — The list of tags specified by the model's maintainer.

Choose another global filter

  All Large Language Models   LMSYS ChatBot Arena ELO   OpenLLM LeaderBoard v1   OpenLLM LeaderBoard v2   Original & Foundation LLMs   OpenCompass LeaderBoard   Recently Added Models   Code Generating Models   Instruction-Based LLMs   Uncensored LLMs   LLMs Fit in 4GB RAM   LLMs Fit in 8GB RAM   LLMs Fit in 12GB RAM   LLMs Fit in 24GB RAM   LLMs Fit in 32GB RAM   GGUF Quantized Models   GPTQ Quantized Models   EXL2 Quantized Models   Fine-Tuned Models   LLMs for Commercial Use   TheBloke's Models   Context Size >16K Tokens   Mixture-Of-Experts Models   Apple's MLX LLMs   Small Language Models
Email us: info@extractum.io. Our Privacy Policy  | Terms and Conditions  | Suggest an improvement.
Our Social Media →  
Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
  Support LLM Explorer