Search Results for "python-gtksourceview2" - Page 12
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Showing 291 open source projects for "python-gtksourceview2"
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Improved GAN
Code for the paper "Improved Techniques for Training GANs"
Improved-GAN is the official code release from OpenAI accompanying the research paper Improved Techniques for Training GANs. It provides implementations of experiments conducted on datasets such as MNIST, SVHN, CIFAR-10, and ImageNet. The project focuses on demonstrating enhanced training methods for Generative Adversarial Networks, addressing stability and performance issues that were common in earlier GAN models. The repository includes training scripts, evaluation methods, and pretrained... -
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InfoGAN
Code for reproducing key results in the paper
The InfoGAN repository contains the original implementation used to reproduce the results in the paper “InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets”. InfoGAN is a variant of the GAN (Generative Adversarial Network) architecture that aims to learn disentangled and interpretable latent representations by maximizing the mutual information between a subset of the latent codes and the generated outputs. That extra incentive encourages the... -
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SG2Im
Code for "Image Generation from Scene Graphs", Johnson et al, CVPR 201
sg2im is a research codebase that learns to synthesize images from scene graphs—structured descriptions of objects and their relationships. Instead of conditioning on free-form text alone, it leverages graph structure to control layout and interactions, generating scenes that respect constraints like “person left of dog” or “cup on table.” The pipeline typically predicts object layouts (bounding boxes and masks) from the graph, then renders a realistic image conditioned on those layouts.... -
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Retrieval-Based Conversational Model
Dual LSTM Encoder for Dialog Response Generation
Retrieval-Based Conversational Model in Tensorflow is a project implementing a retrieval-based conversational model using a dual LSTM encoder architecture in TensorFlow, illustrating how neural networks can be trained to select appropriate responses from a fixed set of candidate replies rather than generate them from scratch. The core idea is to embed both the conversation context and potential replies into vector representations, then score how well each candidate fits the current dialogue,... -
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Mistral Small 4
Model that fuses instruct, reasoning and agentic skills
The Mistral Small 4 collection is a set of open-weight large language models developed by Mistral AI that aim to unify multiple capabilities, including instruction following, reasoning, and coding, within a single efficient architecture. These models are part of the broader Mistral Small family, which is designed to deliver strong performance across a wide range of everyday AI tasks while maintaining relatively low latency and efficient deployment requirements. The collection reflects an... -
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Leanstral
Open-source code agent designed for Lean 4
Leanstral is an open-weight large language model developed by Mistral AI and specifically designed as a code agent for the Lean 4 proof assistant, enabling advanced interaction with formal mathematics and program verification systems. The model is built to understand and generate Lean 4 code, which is used to express complex mathematical constructs as well as formal software specifications. By focusing on theorem proving and formal reasoning, Leanstral represents a specialized direction... -
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Nemotron 3 Nano
LL model providing reasoning and conversational capabilities
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 is a mid-sized open large language model created by NVIDIA to provide strong reasoning and conversational capabilities while maintaining efficient deployment requirements. The model contains roughly 30 billion parameters and is designed to balance performance and computational efficiency, making it suitable for developers building AI applications that cannot run extremely large models. It is trained from scratch and built using a hybrid architecture that... -
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Nemotron 3 Super
Open language model developed by NVIDIA as part of Nemotron-3 family
NVIDIA-Nemotron-3-Super-120B-A12B-FP8 is a large-scale open language model developed by NVIDIA as part of the Nemotron-3 family of generative AI systems designed for advanced reasoning, conversational interaction, and agent-based workflows. The model contains approximately 120 billion parameters, but employs a Mixture-of-Experts architecture that activates only a smaller subset of parameters during inference, improving computational efficiency while maintaining high capability. Its... -
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Mellum-4b-base
JetBrains’ 4B parameter code model for completions
...While the base model is not fine-tuned for downstream tasks, it is designed to be easily adapted through supervised fine-tuning (SFT) or reinforcement learning (RL). Benchmarks on RepoBench, SAFIM, and HumanEval demonstrate its competitive performance, with specialized fine-tuned versions for Python already showing strong improvements. -
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Dia-1.6B
Dia-1.6B generates lifelike English dialogue and vocal expressions
...It is optimized for English and built for real-time performance on enterprise GPUs, though CPU and quantized versions are planned. The format supports [S1]/[S2] tags to differentiate speakers and integrates easily into Python workflows. While not tuned to a specific voice, user-provided audio can guide output style. Licensed under Apache 2.0, Dia is intended for research and educational use, with explicit restrictions on misuse like identity mimicry or deceptive content. -
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gpt-oss-20b
OpenAI’s compact 20B open model for fast, agentic, and local use
GPT-OSS-20B is OpenAI’s smaller, open-weight language model optimized for low-latency, agentic tasks, and local deployment. With 21B total parameters and 3.6B active parameters (MoE), it fits within 16GB of memory thanks to native MXFP4 quantization. Designed for high-performance reasoning, it supports Harmony response format, function calling, web browsing, and code execution. Like its larger sibling (gpt-oss-120b), it offers adjustable reasoning depth and full chain-of-thought visibility... -
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Hunyuan-MT-7B
Tencent’s 36-language state-of-the-art translation model
Hunyuan-MT-7B is a large-scale multilingual translation model developed by Tencent, designed to deliver state-of-the-art translation quality across 36 languages, including several Chinese ethnic minority languages. It forms part of the Hunyuan Translation Model family, alongside Hunyuan-MT-Chimera, which ensembles outputs for even higher accuracy. Trained with a comprehensive framework spanning pretraining, cross-lingual pretraining, supervised fine-tuning, enhancement, and ensemble... -
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mms-300m-1130-forced-aligner
CTC-based forced aligner for audio-text in 158 languages
...The model enables accurate word- or phoneme-level timestamping using Connectionist Temporal Classification (CTC) emissions. Unlike other tools, it provides significant memory efficiency compared to the TorchAudio forced alignment API. Users can integrate it easily through the Python package ctc-forced-aligner, and it supports GPU acceleration via PyTorch. The alignment pipeline includes audio processing, emission generation, tokenization, and span detection, making it suitable for speech analysis, transcription syncing, and dataset creation. This model is especially useful for researchers and developers working with low-resource languages or building multilingual speech systems. -
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DeepSeek-V3.2-Speciale
High-compute ultra-reasoning model surpassing model surpassing GPT-5
DeepSeek-V3.2-Speciale is the high-compute, ultra-reasoning variant of DeepSeek-V3.2, designed specifically to push the boundaries of mathematical, logical, and algorithmic intelligence. It builds on the DeepSeek Sparse Attention (DSA) framework, delivering dramatically improved long-context efficiency while preserving full model quality. Unlike the standard version, Speciale is tuned exclusively for deep reasoning and therefore does not support tool-calling, focusing its full capacity on... -
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DeepSeek-V3.2
High-efficiency reasoning and agentic intelligence model
DeepSeek-V3.2 is a cutting-edge large language model developed by DeepSeek-AI, focused on achieving high reasoning accuracy and computational efficiency for agentic tasks. It introduces DeepSeek Sparse Attention (DSA), a new attention mechanism that dramatically reduces computational overhead while maintaining strong long-context performance. Built with a scalable reinforcement learning framework, it reaches near-GPT-5 levels of reasoning and outperforms comparable models like DeepSeek-V3.1... -
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OpenVLA 7B
Vision-language-action model for robot control via images and text
...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.
