Showing 86 open source projects for "mysql-python"

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  • 1
    ⓍTTS-v2

    ⓍTTS-v2

    Multilingual voice cloning TTS model with 6-second sample support

    ... rate. It's ideal for both inference and fine-tuning, with APIs and command-line tools available. The model powers Coqui Studio and the Coqui API, and can be run locally using Python or through Hugging Face Spaces. Licensed under the Coqui Public Model License, it balances open access with responsible use of generative voice technology.
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  • 2
    GPT-2

    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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  • 3
    whisper-large-v3-turbo

    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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  • 4
    Llama-3.3-70B-Instruct

    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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  • 5
    Llama-2-70b-chat-hf

    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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  • 6
    bge-m3

    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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  • 7
    Llama-2-7b-hf

    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...
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  • 8
    OpenVLA 7B

    OpenVLA 7B

    Vision-language-action model for robot control via images and text

    ... supports real-world robotics tasks, with robust generalization to environments seen in pretraining. Its actions include delta values for position, orientation, and gripper status, and can be un-normalized based on robot-specific statistics. OpenVLA is MIT-licensed, fully open-source, and designed collaboratively by Stanford, Berkeley, Google DeepMind, and TRI. Deployment is facilitated via Python and Hugging Face tools, with flash attention support for efficient inference.
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  • 9
    voice-activity-detection

    voice-activity-detection

    Detects speech activity in audio using pyannote.audio 2.1 pipeline

    The voice-activity-detection model by pyannote is a neural pipeline for detecting when speech occurs in audio recordings. Built on pyannote.audio 2.1, it identifies segments of active speech within any audio file, making it valuable for preprocessing tasks like transcription, diarization, or voice-controlled systems. The model was trained using datasets such as AMI, DIHARD, and VoxConverse, and it requires users to authenticate via Hugging Face for access. To use the model, users must accept...
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  • 10
    chronos-t5-small

    chronos-t5-small

    Time series forecasting model using T5 architecture with 46M params

    ... probabilistic forecasting by autoregressively sampling multiple future trajectories. The model is capable of generating full predictive distributions, making it well-suited for uncertainty-aware forecasting. It is compatible with the Chronos Python package and integrates easily into forecasting pipelines using PyTorch. Chronos models are open-source under Apache 2.0 and have been demonstrated to perform competitively in forecasting benchmarks.
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  • 11
    ERNIE-4.5-VL-28B-A3B-Paddle

    ERNIE-4.5-VL-28B-A3B-Paddle

    Multimodal ERNIE 4.5 MoE model for image-text reasoning and chat

    ERNIE-4.5-VL-28B-A3B-Paddle is a multimodal MoE chat model designed for complex image-text tasks, featuring 28 billion total parameters with 3 billion activated per token. Built on PaddlePaddle, it excels in tasks like visual question answering, description generation, and multimodal reasoning. It employs a heterogeneous Mixture-of-Experts architecture that supports both thinking and non-thinking inference modes. The model benefits from advanced pretraining and posttraining strategies,...
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