Search Results for "raspberry-gpio-python" - Page 3
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Showing 64 open source projects for "raspberry-gpio-python"
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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... -
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GPT-2
GPT-2 is a 124M parameter English language model for text generation
GPT-2 is a pretrained transformer-based language model developed by OpenAI for generating natural language text. Trained on 40GB of internet data from outbound Reddit links (excluding Wikipedia), it uses causal language modeling to predict the next token in a sequence. The model was trained without human labels and learns representations of English that support text generation, feature extraction, and fine-tuning. GPT-2 uses a byte-level BPE tokenizer with a vocabulary of 50,257 and handles... -
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whisper-large-v3-turbo
Whisper-large-v3-turbo delivers fast, multilingual speech recognition
Whisper-large-v3-turbo is a high-performance automatic speech recognition (ASR) and translation model developed by OpenAI, based on a pruned version of Whisper large-v3. It reduces decoding layers from 32 to 4, offering significantly faster inference with only minor degradation in accuracy. Trained on over 5 million hours of multilingual data, it handles speech transcription, translation, and language identification across 99 languages. It supports advanced decoding strategies like beam... -
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Llama-3.3-70B-Instruct
Llama-3.3-70B-Instruct is a multilingual AI optimized for helpful chat
Llama-3.3-70B-Instruct is Meta's large, instruction-tuned language model designed for safe, multilingual, assistant-style conversations and text generation. With 70 billion parameters, it supports English, Spanish, French, German, Italian, Portuguese, Hindi, and Thai, offering state-of-the-art performance across a wide range of benchmarks including MMLU, HumanEval, and GPQA. The model is built on a transformer architecture with grouped-query attention, trained on over 15 trillion tokens and... -
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Llama-2-70b-chat-hf
Llama-2-70B-Chat is Meta’s largest fine-tuned open-source chat LLM
Llama-2-70B-Chat is Meta’s largest fine-tuned large language model, optimized for dialogue and aligned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). It features 70 billion parameters and uses a transformer architecture with grouped-query attention (GQA) to improve inference scalability. Trained on 2 trillion tokens from publicly available sources and over a million human-annotated examples, the model outperforms most open-source chat models and... -
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bge-m3
BGE-M3 is a multilingual embedding model
BGE-M3 is an advanced text embedding model developed by BAAI that excels in multi-functionality, multi-linguality, and multi-granularity. It supports dense retrieval, sparse retrieval (lexical weighting), and multi-vector retrieval (ColBERT-style), making it ideal for hybrid systems in retrieval-augmented generation (RAG). The model handles over 100 languages and supports long-text inputs up to 8192 tokens, offering flexibility across short queries and full documents. BGE-M3 was trained... -
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Llama-2-7b-hf
Llama-2-7B is a 7B-parameter transformer model for text generation
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 tasks. It is a 7 billion parameter auto-regressive transformer trained on 2 trillion tokens from publicly available sources, using an optimized architecture without Grouped-Query Attention (GQA). This model is the pretrained version, intended for research and commercial use in English, and can be adapted for downstream applications such as...
