Showing 179 open source projects for "python to executable file"

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  • 1
    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...
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  • 2
    Some NLP experiments starting with a tokenization attempt in Python. The code tokenite.py reads a text file "blog1.txt" and tries to tokenize it. The code doesnot work as is, but is almost on the verge of working. Any suggestions will be greatly appreciated. I define a class called text and define methods inside it. The method count defines a generator which I use in the method named t_tok. But if you look closely at 66 to 72 you will see that I am modifying the outer limit of the for loop...
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  • 3
    ControlNet

    ControlNet

    Extension for Stable Diffusion using edge, depth, pose, and more

    ..., including Canny edges, HED (soft edges), Midas depth, OpenPose skeletons, normal maps, MLSD lines, scribbles, and ADE20k-based semantic segmentation. The system includes both ControlNet+SD1.5 model weights and compatible third-party detectors like Midas and OpenPose to extract input features. Each conditioning type is matched with a specific .pth model file to be used alongside Stable Diffusion for fine-grained control.
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  • 4
    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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