Showing 5 open source projects for "python file"

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  • 1
    GPT Discord Bot

    GPT Discord Bot

    Example Discord bot written in Python that uses the completions API

    GPT Discord Bot is an example project from OpenAI that shows how to integrate the OpenAI API with Discord using Python. The bot uses the Chat Completions API (defaulting to gpt-3.5-turbo) to carry out conversational interactions and the Moderations API to filter user messages. It is built on top of the discord.py framework and the OpenAI Python library, providing a simple, extensible template for building AI-powered Discord applications. The bot supports a /chat command that spawns a public...
    Downloads: 0 This Week
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  • 2
    rwkv.cpp

    rwkv.cpp

    INT4/INT5/INT8 and FP16 inference on CPU for RWKV language model

    Besides the usual FP32, it supports FP16, quantized INT4, INT5 and INT8 inference. This project is focused on CPU, but cuBLAS is also supported. RWKV is a novel large language model architecture, with the largest model in the family having 14B parameters. In contrast to Transformer with O(n^2) attention, RWKV requires only state from the previous step to calculate logits. This makes RWKV very CPU-friendly on large context lengths.
    Downloads: 0 This Week
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  • 3
    Grok-1

    Grok-1

    Open-source, high-performance Mixture-of-Experts large language model

    ... a machine with significant GPU memory. The repository's MoE layer implementation prioritizes correctness over efficiency, avoiding the need for custom kernels. This is a full repo snapshot ZIP file of the Grok-1 code.
    Downloads: 11 This Week
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  • 4
    DeepSWE-Preview

    DeepSWE-Preview

    State-of-the-art RL-trained coding agent for complex SWE tasks

    DeepSWE-Preview is a 32.8B parameter open-source coding agent trained solely with reinforcement learning (RL) to perform complex software engineering (SWE) tasks. Built on top of Qwen3-32B, it achieves 59% accuracy on the SWE-Bench-Verified benchmark—currently the highest among open-weight models. The model navigates and edits large codebases using tools like a file editor, bash execution, and search, within the R2E-Gym environment. Its training emphasizes sparse reward signals, test-time...
    Downloads: 0 This Week
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  • 5
    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...
    Downloads: 0 This Week
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