PokeeResearch - 7B is a 7 - billion - parameter in - depth research agent model developed by Pokee AI. It combines Reinforcement Learning from AI Feedback (RLAIF) and a powerful reasoning framework, enabling reliable, aligned, and scalable research - level reasoning in tool - enhanced large language models. It is suitable for complex multi - step research workflows.

Natural Language ProcessingTransformersEnglish

This is the quantized version of PokeeAI's pokee_research_7b model, using llamacpp for iMatrix quantization, which can effectively improve the running efficiency of the model on different hardware and allow users to experience the powerful capabilities of the model in a low-resource environment.

Natural Language ProcessingGguf

This is the quantized version of the PokeeAI/pokee_research_7b model, adhering to the concept of 'Making knowledge accessible to everyone', and reducing the resource requirements for model deployment and use through quantization technology.

Natural Language ProcessingGguf

PokeeResearch-7B is a 7-billion-parameter deep research intelligent agent developed by Pokee AI. It combines reinforcement learning based on AI feedback (RLAIF) with an inference framework and can execute complex multi-step research workflows, including self-correction, verification, and comprehensive analysis.

Natural Language ProcessingTransformersEnglish

A 1.7B parameter fine-tuned model based on the Qwen3 architecture, using Unsloth and the Huggingface TRL library for efficient training, with a 2-fold increase in training speed, focusing on English text generation tasks.

Natural Language ProcessingTransformersEnglish

This is a fine-tuned ViT (Vision Transformer) model specifically designed for Pokémon image classification. The model has been trained to classify Pokémon images up to the ninth generation (1025 species).

Computer VisionTransformersEnglish

A Pokemon image classification model based on the Vision Transformer (ViT) architecture, capable of classifying Pokemon images into 18 different attributes.

Computer VisionTransformers

A LoRA model trained on poker cards from the popular game 'Balatro' for generating pixel-style poker card images.

Computer VisionDiffusers

A LoRA model trained on 96x96 Pokémon sprites and BLIP image captions for generating Pokémon trainer-style pixel images

MultimodalDiffusers

A Pokemon image generation model based on PyTorch and Diffusers library, using Denoising Diffusion Probabilistic Model (DDPM) architecture

Computer VisionDiffusers

This is a multimodal model fine-tuned based on Microsoft's Kosmos-2 model, specifically designed for recognizing Pokemon names on Pokemon cards.

MultimodalTransformersEnglish

LoRA model trained on 96x96 Pokémon sprites and BLIP image captions for generating pixel-style Pokémon trainer characters

Computer VisionDiffusers

An image classification model fine-tuned on the Pokemon classification dataset based on Google's ViT model

Computer VisionTransformers

A fine-tuned model based on facebook/detr-resnet-50 for object detection tasks

Computer VisionTransformers

An image caption generation model fine-tuned from microsoft/git-base, trained on Pokemon image dataset

MultimodalTransformersOther

This is a Pokemon image classification model based on the Swin Transformer architecture, capable of recognizing different Pokemon in the selection interface.

Computer VisionTransformers

A LoRA fine-tuned version based on the CompVis/stable-diffusion-v1-4 model, specifically designed for generating Pokemon-style images.

Computer VisionDiffusers

A vision classification model fine-tuned based on google/vit-base-patch16-224 for recognizing first-generation Pokémon

Computer VisionTransformers

LoRA adapter weights trained on Stable Diffusion v1-5, specifically designed for generating Pokemon-style images.

Computer VisionDiffusers

A text-to-image generation model fine-tuned using LoRA technology based on Stable Diffusion v1-4, specifically optimized for the pokemon-blip-captions dataset.

MultimodalDiffusers