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Wiro AI

Social Media & ViralEcommerceHuman ResourcesFast Inference

Video Generation

Text to VideoImage to VideoSpeech to VideoTalking Head

Image Generation

Text to ImageImage to ImageImage Editing

Audio & Speech

Text to SpeechSpeech to TextText to MusicText to SongImage to SongVideo to MusicVoice Clone

LLM & Chat

ChatLLMPartner LLMRAGReasoning

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GLM-4.7-Flash - AI Tool Cover Image
Chat

zai-org/GLM-4.7-Flash

A chat-based language model optimized for conversational AI tasks with support for custom prompts and session management.
1
rag-chat-github - AI Tool Cover Image
Chat

wiro/rag-chat-github

Instantly retrieve and analyze content from any GitHub repository. Select your LLM model, extract relevant information from codebases or documentation, and generate context-aware responses with ease!
0
rag-chat-youtube - AI Tool Cover Image
Chat

wiro/rag-chat-youtube

Extract insights directly from YouTube videos by simply providing a URL. Choose your LLM model, access video transcripts or summaries, and create contextually rich conversations effortlessly!
0
rag-chat-website - AI Tool Cover Image
Chat

wiro/rag-chat-website

Instantly retrieve and analyze content from any website URL. Select your LLM model, fetch key information from the page, and generate context-aware responses with ease!
0
rag-chat - AI Tool Cover Image
Chat

wiro/rag-chat

Wiro LLM Conversational RAG Tool – A unified interface for seamless RAG-based conversations. Select your preferred LLM model, retrieve relevant data from external sources, and generate context-aware responses effortlessly!
0
chat - AI Tool Cover Image
Chat

wiro/chat

Wiro LLM Conversational chat script! Runs every LLM with one tool.
1
wiroai-turkish-llm-9b - AI Tool Cover Image
Chat

wiro/wiroai-turkish-llm-9b

Meet with Wiro AI's Turkish Large Language Model (LLM)! A robust language model with more Turkish language and culture support!
0
gpt-oss-20b - AI Tool Cover Image
Chat

openai/gpt-oss-20b

OpenAI GPT-OSS-20B model.
0
Qwen3-30B-A3B - AI Tool Cover Image
Chat

Qwen/Qwen3-30B-A3B

Qwen3-30B-A3B model.
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Qwen3-30B-A3B-Thinking-2507 - AI Tool Cover Image
Chat

Qwen/Qwen3-30B-A3B-Thinking-2507

Qwen3-30B-A3B-Thinking-2507 model.
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Qwen3-32B - AI Tool Cover Image
Chat

Qwen/Qwen3-32B

Qwen3-32B model.
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Qwen3-Coder-30B-A3B-Instruct - AI Tool Cover Image
Chat

Qwen/Qwen3-Coder-30B-A3B-Instruct

Qwen3-Coder-30B-A3B-Instruct model.
0
SenecaLLM-x-QwQ-32B-Q8_Max-Version - AI Tool Cover Image
Chat

AlicanKiraz0/SenecaLLM-x-QwQ-32B-Q8_Max-Version

Mixed dataset LLM Information Security v1.5 - Incident Response v1.3.1 - Threat Hunting v1.3.2 - Ethical Exploit Development v2.0 - Purple Team Tactics v1.3 - Reverse Engineering v2.0
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Qwen2.5-14B-Instruct - AI Tool Cover Image
Chat

Qwen/Qwen2.5-14B-Instruct

Qwen2.5-14B-Instruct is a large language model by Alibaba’s Qwen team, optimized for instruction-following tasks with 14 billion parameters. It offers strong reasoning, multilingual capabilities, and efficient performance, making it suitable for chatbots, content creation, and various AI-driven applications.
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Qwen2.5-32B-Instruct - AI Tool Cover Image
Chat

Qwen/Qwen2.5-32B-Instruct

Qwen2.5-32B-Instruct is a powerful large language model developed by Alibaba’s Qwen team, designed for instruction-following tasks with enhanced reasoning and natural language understanding capabilities. Optimized for efficiency and accuracy, it supports multi-turn conversations and complex queries, making it suitable for applications such as chatbots, content generation, and AI assistants.
0
DeepSeek-R1-Distill-Qwen-32B - AI Tool Cover Image
Chat

deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

DeepSeek-R1-Distill-Qwen-32B is a distilled version of the DeepSeek-R1 model with 32 billion parameters, leveraging the Qwen architecture to deliver high-quality language understanding and generation. Through knowledge distillation, it retains the strengths of larger models while offering improved efficiency and reduced computational requirements. This model is ideal for large-scale AI applications that demand robust performance with optimized resource utilization.
0
DeepSeek-R1-Distill-Qwen-14B - AI Tool Cover Image
Chat

deepseek-ai/DeepSeek-R1-Distill-Qwen-14B

DeepSeek-R1-Distill-Qwen-14B is a distilled version of the DeepSeek-R1 model, featuring 14 billion parameters. Built on the Qwen architecture, it utilizes advanced knowledge distillation techniques to achieve a balance between high performance and computational efficiency. This model is well-suited for a wide range of natural language processing tasks, providing accurate and context-aware responses while optimizing resource consumption for deployment in production environments.
0
DeepSeek-R1-Distill-Llama-8B - AI Tool Cover Image
Chat

deepseek-ai/DeepSeek-R1-Distill-Llama-8B

DeepSeek-R1-Distill-Llama-8B is a distilled version of the DeepSeek-R1 model based on the LLaMA architecture, featuring 8 billion parameters. It offers a balance between performance and efficiency by leveraging knowledge distillation techniques to reduce computational costs while maintaining high-quality language processing capabilities. This model is ideal for applications that require powerful text generation and understanding with optimized resource usage.
0
DeepSeek-R1-Distill-Qwen-7B - AI Tool Cover Image
Chat

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

DeepSeek-R1-Distill-Qwen-7B is a distilled version of the DeepSeek-R1 model with 7 billion parameters, designed to provide high-quality language understanding while optimizing efficiency. Leveraging advanced knowledge distillation techniques, it retains the core capabilities of larger models with improved speed and lower resource consumption. This model is well-suited for tasks requiring robust natural language processing while maintaining cost-effective deployment.
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DeepSeek-R1-Distill-Qwen-1.5B - AI Tool Cover Image
Chat

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

DeepSeek-R1-Distill-Qwen-1.5B is a distilled version of the DeepSeek-R1 model, designed to offer a balance between efficiency and performance. With 1.5 billion parameters, it leverages knowledge distillation techniques to retain the capabilities of larger models while optimizing for faster inference and reduced resource consumption. Ideal for applications requiring high-quality language understanding with lower computational overhead.
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