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Showing 1432 open source projects for "compiler python linux"
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Cellicone is a project to develop an artificial life organism with the necessary components to make it comparable to biological life as we know it. This includes components ranging from proteins to cells to organs to limbs, and many steps between.
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ERNIE-4.5-300B-A47B-FP8-Paddle
ERNIE 4.5 MoE model in FP8 for efficient high-performance inference
.... It is especially well-suited for production environments requiring high throughput and lower memory use, while maintaining high reasoning and generation quality. The model can be used with FastDeploy and integrates cleanly with Python APIs for prompt-based generation workflows. It supports long context lengths (up to 131,072 tokens) and includes both Chinese and English prompt templates for web search applications. -
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starcoder
Code generation model trained on 80+ languages with FIM support
StarCoder is a 15.5B parameter language model developed by BigCode for code generation tasks across more than 80 programming languages. It is trained on 1 trillion tokens from the permissively licensed dataset The Stack v1.2, using the Fill-in-the-Middle (FIM) objective and Multi-Query Attention to enhance performance. With an extended context window of 8192 tokens and pretraining in bfloat16, StarCoder can generate, complete, or refactor code in various languages, with English as the... -
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Savant
Python Computer Vision & Video Analytics Framework With Batteries Incl
Savant is an open-source, high-level framework for building real-time, streaming, highly efficient multimedia AI applications on the Nvidia stack. It helps to develop dynamic, fault-tolerant inference pipelines that utilize the best Nvidia approaches for data center and edge accelerators. Savant is built on DeepStream and provides a high-level abstraction layer for building inference pipelines. It is designed to be easy to use, flexible, and scalable. It is a great choice for building... -
Picsart Enterprise Background Removal API for Stunning eCommerce VisualsWith our Remove Background API tool, you can access the transformative capabilities of automation , which will allow you to turn any photo asset into compelling product imagery. With elevated visuals quality on your digital platforms, you can captivate your audience, and therefore achieve higher engagement and sales.
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segmentation-3.0
Speaker segmentation model for 10s audio chunks with powerset labels
segmentation-3.0 is a voice activity and speaker segmentation model from the pyannote.audio framework, designed to analyze 10-second mono audio sampled at 16kHz. It outputs a (num_frames, num_classes) matrix using a powerset encoding that includes non-speech, individual speakers, and overlapping speech for up to three speakers. Trained with pyannote.audio 3.0.0 on a rich blend of datasets—including AISHELL, DIHARD, VoxConverse, and more—it enables downstream tasks like voice activity... -
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MiniMax-M1
Open-weight, large-scale hybrid-attention reasoning model
MiniMax-M1 is the world’s first open-weight, large-scale hybrid-attention reasoning model designed for long-context and complex reasoning tasks. Powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism, it efficiently supports context lengths up to 1 million tokens—eight times larger than many contemporary models. MiniMax-M1 significantly reduces computational overhead at generation time, consuming only about 25% FLOPs compared to comparable... -
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Nanonets-OCR-s
State-of-the-art image-to-markdown OCR model
Nanonets-OCR-s is an advanced image-to-markdown OCR model that transforms documents into structured and semantically rich markdown. It goes beyond basic text extraction by intelligently recognizing content types and applying meaningful tags, making the output ideal for Large Language Models (LLMs) and automated workflows. The model expertly converts mathematical equations into LaTeX syntax, distinguishing between inline and display modes for accuracy. It also generates descriptive <img> tags... -
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FLUX.1-dev
Powerful 12B parameter model for top-tier text-to-image creation
FLUX.1-dev is a powerful 12-billion parameter rectified flow transformer designed for generating high-quality images from text prompts. It delivers cutting-edge output quality, just slightly below the flagship FLUX.1 [pro] model, and matches or exceeds many closed-source competitors in prompt adherence. The model is trained using guidance distillation, making it more efficient and accessible for developers and artists alike. FLUX.1-dev is openly available with weights provided to support... -
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stable-diffusion-v1-4
Text-to-image diffusion model for high-quality image generation
stable-diffusion-v1-4 is a high-performance text-to-image latent diffusion model developed by CompVis. It generates photo-realistic images from natural language prompts using a pretrained CLIP ViT-L/14 text encoder and a UNet-based denoising architecture. This version builds on v1-2, fine-tuned over 225,000 steps at 512×512 resolution on the “laion-aesthetics v2 5+” dataset, with 10% text-conditioning dropout for improved classifier-free guidance. It is optimized for use with Hugging Face’s... -
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stable-diffusion-xl-base-1.0
Advanced base model for high-quality text-to-image generation
stable-diffusion-xl-base-1.0 is a next-generation latent diffusion model developed by Stability AI for producing highly detailed images from text prompts. It forms the core of the SDXL pipeline and can be used on its own or paired with a refinement model for enhanced results. This base model utilizes two pretrained text encoders—OpenCLIP-ViT/G and CLIP-ViT/L—for richer text understanding and improved image quality. The model supports two-stage generation, where the base model creates initial... -
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stable-diffusion-3-medium
Efficient text-to-image model with enhanced quality and typography
Stable Diffusion 3 Medium is a next-generation text-to-image model by Stability AI, designed using a Multimodal Diffusion Transformer (MMDiT) architecture. It offers notable improvements in image quality, prompt comprehension, typography, and computational efficiency over previous versions. The model integrates three fixed, pretrained text encoders—OpenCLIP-ViT/G, CLIP-ViT/L, and T5-XXL—to interpret complex prompts more effectively. Trained on 1 billion synthetic and filtered public images,... -
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Kokoro-82M
Lightweight, fast, and high-quality open TTS model with 82M params
Kokoro-82M is an open-weight, lightweight text-to-speech (TTS) model featuring 82 million parameters, developed to deliver high-quality voice synthesis with exceptional efficiency. Despite its compact size, Kokoro rivals the output quality of much larger models while remaining significantly faster and cheaper to run. Built on StyleTTS2 and ISTFTNet architectures, it uses a decoder-only setup without diffusion, enabling rapid audio generation with low computational overhead. Kokoro supports... -
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whisper-large-v3
High-accuracy multilingual speech recognition and translation model
Whisper-large-v3 is OpenAI’s most advanced multilingual automatic speech recognition (ASR) and speech translation model, featuring 1.54 billion parameters and trained on 5 million hours of labeled and pseudo-labeled audio. Built on a Transformer-based encoder-decoder architecture, it supports 99 languages and delivers significant improvements in transcription accuracy, robustness to noise, and handling of diverse accents. Compared to previous versions, v3 introduces a 128 Mel bin spectrogram... -
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Llama-2-7b-chat-hf
Dialogue-optimized 7B language model for safe and helpful chatting
Llama-2-7b-chat-hf is a fine-tuned large language model developed by Meta, designed specifically for dialogue use cases. With 7 billion parameters and built on an optimized transformer architecture, it uses supervised fine-tuning and reinforcement learning with human feedback (RLHF) to enhance helpfulness, coherence, and safety. It outperforms most open-source chat models and rivals proprietary systems like ChatGPT in human evaluations. Trained on 2 trillion tokens of public text and over 1... -
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Llama-2-7b
7B-parameter foundational LLM by Meta for text generation tasks
Llama-2-7B is a foundational large language model developed by Meta as part of the Llama 2 family, designed for general-purpose text generation in English. It has 7 billion parameters and uses an optimized transformer-based, autoregressive architecture. Trained on 2 trillion tokens of publicly available data, it serves as the base for fine-tuned models like Llama-2-Chat. The model is pretrained only, meaning it is not optimized for dialogue but can be adapted for various natural language... -
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Llama-3.1-8B-Instruct
Multilingual 8B-parameter chat-optimized LLM fine-tuned by Meta
Llama-3.1-8B-Instruct is a multilingual, instruction-tuned language model developed by Meta, designed for high-quality dialogue generation across eight languages, including English, Spanish, French, German, Italian, Portuguese, Hindi, and Thai. It uses a transformer-based, autoregressive architecture with Grouped-Query Attention and supports a 128k token context window. The model was fine-tuned using a combination of supervised fine-tuning (SFT), reinforcement learning with human feedback... -
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Meta-Llama-3-8B-Instruct
Instruction-tuned 8B LLM by Meta for helpful, safe English dialogue
Meta-Llama-3-8B-Instruct is an instruction-tuned large language model from Meta’s Llama 3 family, optimized for safe and helpful English dialogue. It uses an autoregressive transformer architecture with Grouped-Query Attention (GQA) and supports an 8k token context length. Fine-tuned using supervised learning and reinforcement learning with human feedback (RLHF), the model achieves strong results on benchmarks like MMLU, GSM8K, and HumanEval. Trained on over 15 trillion tokens of publicly... -
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FLUX.1-schnell
12B-parameter image generator using fast rectified flow transformers
FLUX.1-schnell is a 12 billion parameter text-to-image model developed by Black Forest Labs, designed for high-quality image generation using rectified flow transformers. It produces competitive visual results with strong prompt adherence, rivaling closed-source models in just 1 to 4 inference steps. Trained using latent adversarial diffusion distillation, the model is optimized for both quality and speed. It is released under the Apache 2.0 license, allowing commercial, scientific, and... -
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stable-diffusion-2-1
Latent diffusion model for high-quality text-to-image generation
Stable Diffusion 2.1 is a text-to-image generation model developed by Stability AI, building on the 768-v architecture with additional fine-tuning for improved safety and image quality. It uses a latent diffusion framework that operates in a compressed image space, enabling faster and more efficient image synthesis while preserving detail. The model is conditioned on text prompts via the OpenCLIP-ViT/H encoder and supports generation at resolutions up to 768×768. Released under the... -
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ControlNet
Extension for Stable Diffusion using edge, depth, pose, and more
ControlNet is a neural network architecture that enhances Stable Diffusion by enabling image generation conditioned on specific visual structures such as edges, poses, depth maps, and segmentation masks. By injecting these auxiliary inputs into the diffusion process, ControlNet gives users powerful control over the layout and composition of generated images while preserving the style and flexibility of generative models. It supports a wide range of conditioning types through pretrained... -
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phi-2
Small, high-performing language model for QA, chat, and code tasks
Phi-2 is a 2.7 billion parameter Transformer model developed by Microsoft, designed for natural language processing and code generation tasks. It was trained on a filtered dataset of high-quality web content and synthetic NLP texts created by GPT-3.5, totaling 1.4 trillion tokens. Phi-2 excels in benchmarks for common sense, language understanding, and logical reasoning, outperforming most models under 13B parameters despite not being instruction-tuned or aligned via RLHF. It performs best... -
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stable-video-diffusion-img2vid-xt
Generates high-quality short videos from a single still image input
Stable Video Diffusion Img2Vid XT is an advanced image-to-video latent diffusion model developed by Stability AI, designed to generate short video clips from a single static image. It produces 25 frames at 576x1024 resolution, offering improved temporal consistency by fine-tuning from an earlier 14-frame version. The model operates without text prompts and instead uses a single input frame to guide visual generation, making it ideal for stylized motion or animation. It includes both a... -
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stable-diffusion-3.5-large
Advanced MMDiT text-to-image model for high-quality visual generation
Stable Diffusion 3.5 Large is a multimodal diffusion transformer (MMDiT) developed by Stability AI, designed for generating high-quality images from text prompts. It integrates three pretrained text encoders—OpenCLIP-ViT/G, CLIP-ViT/L, and T5-XXL—with QK-normalization for improved training stability and prompt understanding. This model excels in handling typography, detailed scenes, and creative compositions while maintaining resource efficiency. It supports inference via ComfyUI, Hugging... -
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chatglm-6b
Bilingual 6.2B parameter chatbot optimized for Chinese and English
ChatGLM-6B is a 6.2 billion parameter bilingual language model developed by THUDM, based on the General Language Model (GLM) framework. It is optimized for natural and fluent dialogue in both Chinese and English, supporting applications in conversational AI, question answering, and assistance. Trained on approximately 1 trillion tokens, the model benefits from supervised fine-tuning, feedback self-training, and reinforcement learning with human feedback to align its outputs with human... -
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Mistral-7B-Instruct-v0.2
Instruction-tuned 7B model for chat and task-oriented text generation
Mistral-7B-Instruct-v0.2 is a fine-tuned version of the Mistral-7B-v0.2 language model, designed specifically for following instructions in a conversational format. It supports a 32k token context window, enabling more detailed and longer interactions compared to its predecessor. The model is trained to respond to user prompts formatted with [INST] and [/INST] tags, and it performs well in instruction-following tasks like Q&A, summarization, and explanations. It can be used via the official...
