# Zero-shot Reasoning

This model is based on the transformers library, and its specific functions and uses require further information to confirm.

An 8B-parameter language model based on the Llama 3.1 architecture, created by merging multiple models with mergekit, capable of text generation

An 8B-parameter language model based on the Llama 3.1 architecture, created by merging multiple specialized models, excelling in text generation tasks.

This is a 14B-parameter large language model merged using the SLERP method, combining two base models: li-14b-v0.4 and miscii-14b-0218.

A reasoning model based on Deepseek-R1 knowledge distillation, supporting Chain-of-Thought (CoT) reasoning capabilities

LLaVA-Llama3 is a multimodal model based on Llama-3, supporting joint processing of images and text.

This model is a multilingual text generation model converted from bigscience/mt0-xxl-mt to GGUF format via llama.cpp, supporting various language tasks.

LLaVA-SpaceSGG is a visual question-answering model based on LLaVA-v1.5-13b, focusing on scene graph generation tasks. It can understand image content and generate structured scene descriptions.

A fine-tuned image-to-text generation model based on PaliGemma 224x224 version, specializing in generating detailed descriptions for artistic images

A Flan-T5-xl fine-tuned model for targeted sentiment analysis, supporting sentiment polarity judgment (positive/negative/neutral) in English text

Llava-v1.6-mistral is a vision-language model (VLM)-based medical visual question answering system capable of understanding and answering questions related to medical images.

RWKV-5 Eagle 7B is a 7B-parameter large language model based on the RWKV architecture, supporting Chinese text generation tasks

BLIP2 is a vision-language model based on OPT-2.7B, focusing on visual question answering tasks. It can understand image content and answer related questions.

LiteLlama is a streamlined version of Meta AI's LLaMa 2, featuring only 460 million parameters and trained on 1 trillion tokens as an open-source language model

An instruction-fine-tuned language model based on LLaMA 3B v2 architecture, suitable for text generation tasks

LLaVA is a vision-language model capable of understanding and generating text related to images.

InstructBLIP is a vision instruction tuning model based on BLIP-2, using Flan-T5-xl as the language model, capable of generating descriptions based on images and text instructions.

InstructBLIP is the vision-instruction-tuned version of BLIP-2, based on the Flan-T5-xl language model, designed for image-to-text generation tasks.

BLIP-2 is a vision-language model that combines an image encoder with a large language model for image-to-text tasks.

mPLUG-Owl is a multilingual vision-language model that supports image understanding and multi-turn dialogue, developed based on the BLOOMZ-7B architecture.

mPLUG-Owl is a multimodal large language model based on the LLaMA-7B architecture, supporting image understanding and text generation tasks.

LLaMA is an efficient foundational language model based on the Transformer architecture developed by Meta AI, offering a 7B parameter version that supports multiple language processing tasks.

BLIP-2 is a vision-language model that combines an image encoder with a large language model for image-to-text generation tasks.

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FLAN-T5 is an instruction-fine-tuned language model based on T5, achieving superior performance through fine-tuning on over 1,000 multilingual tasks with the same parameter count

A large language model based on Transformer architecture, supporting text understanding and generation tasks in both Chinese and English

This is a versatile large language model capable of understanding and generating natural language text

XGLM-1.7B is a multilingual autoregressive language model with 1.7 billion parameters, trained on a diverse and balanced corpus of 500 billion subword tokens.

A versatile large language model capable of handling various natural language tasks

XGLM-4.5B is a multilingual autoregressive language model with 4.5 billion parameters, trained on a balanced corpus of 134 languages.

This is a versatile large language model capable of handling various natural language processing tasks