Models (3)·续6
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<h3>llama-3.3-70b-instruct-fp8-fast</h3>MetaText Generation Llama 3.3 70B quantized to fp8 precision, optimized to be faster.
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<h3>llama-4-scout-17b-16e-instruct</h3>MetaText Generation Meta's Llama 4 Scout is a 17 billion parameter model with 16 experts that is natively multimodal. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.
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<h3>llama-guard-3-8b</h3>MetaText Generation Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.
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l<h3>llava-1.5-7b-hf</h3>Betallava-hfImage-to-Text 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.
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<h3>lucid-origin</h3>LeonardoText-to-Image Lucid Origin from Leonardo.AI is their most adaptable and prompt-responsive model to date. Whether you're generating images with sharp graphic design, stunning full-HD renders, or highly specific creative direction, it adheres closely to your prompts, renders text with accuracy, and supports a wide array of visual styles and aesthetics – from stylized concept art to crisp product mockups.
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<h3>m2m100-1.2b</h3>MetaTranslation Multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation
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<h3>melotts</h3>MyShellText-to-Speech MeloTTS is a high-quality multi-lingual text-to-speech library by MyShell.ai.
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<h3>mistral-7b-instruct-v0.2-lora</h3>MistralAIText Generation The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.
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<h3>mistral-small-3.1-24b-instruct</h3>MistralAIText Generation Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
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m<h3>moondream3.1-9B-A2B</h3>moondreamImage-to-Text Moondream 3 is a fast, efficient 9B mixture-of-experts vision language model (2B active parameters) that delivers frontier-level visual reasoning for tasks like object detection, pointing, OCR, and structured output.
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<h3>nemotron-3-120b-a12b</h3>NVIDIAText Generation NVIDIA Nemotron 3 Super is a hybrid MoE model with leading accuracy for multi-agent applications and specialized agentic AI systems.
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<h3>nova-3</h3>DeepgramAutomatic Speech Recognition Transcribe audio using Deepgram’s speech-to-text model
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<h3>phoenix-1.0</h3>LeonardoText-to-Image Phoenix 1.0 is a model by Leonardo.Ai that generates images with exceptional prompt adherence and coherent text.
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d<h3>phonellm-mini-alpha-2</h3>daily-coText Generation PhoneLLM is an open model tuned for voice agents. The model is based on Nemotron Nano 30B base capabilities and excels at typical voice agent tasks with thinking disabled.
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p<h3>plamo-embedding-1b</h3>pfnetText Embeddings PLaMo-Embedding-1B is a Japanese text embedding model developed by Preferred Networks, Inc. It can convert Japanese text input into numerical vectors and can be used for a wide range of applications, including information retrieval, text classification, and clustering.
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<h3>qwen2.5-coder-32b-instruct</h3>QwenText Generation Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
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<h3>qwen3-30b-a3b-fp8</h3>QwenText Generation Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support.
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<h3>qwen3-embedding-0.6b</h3>QwenText Embeddings The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks.
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<h3>qwen3.8-27b</h3>QwenText Generation Qwen 3.8 27B is a 27-billion-parameter instruction-tuned language model from Alibaba's Qwen family, designed for vision, efficient general-purpose text generation and agentic workloads.
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<h3>qwq-32b</h3>QwenText Generation QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.
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<h3>resnet-50</h3>MicrosoftImage Classification 50 layers deep image classification CNN trained on more than 1M images from ImageNet
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<h3>smart-turn-v2</h3>PipecatDumb Pipe An open source, community-driven, native audio turn detection model in 2nd version
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<h3>stable-diffusion-v1-5-inpainting</h3>BetaRunwayMLText-to-Image Stable Diffusion Inpainting is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask.
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<h3>stable-diffusion-xl-base-1.0</h3>BetaStability.aiText-to-Image Diffusion-based text-to-image generative model by Stability AI. Generates and modify images based on text prompts.
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<h3>stable-diffusion-xl-lightning</h3>BetaByteDanceText-to-Image SDXL-Lightning is a lightning-fast text-to-image generation model. It can generate high-quality 1024px images in a few steps.
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<h3>whisper</h3>OpenAIAutomatic Speech Recognition Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.
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<h3>whisper-large-v3-turbo</h3>OpenAIAutomatic Speech Recognition Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation.
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<h3>whisper-tiny-en</h3>BetaOpenAIAutomatic Speech Recognition Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalize to many datasets and domains without the need for fine-tuning. This is the English-only version of the Whisper Tiny model which was trained on the task of speech recognition.
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