Search Results for "pyscripter python 2" - Page 2
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Showing 254 open source projects for "pyscripter python 2"
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1
OpenLLM
Operating LLMs in production
..., CLI, our Python/Javascript client, or any HTTP client. -
2
tf2onnx
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
tf2onnx converts TensorFlow (tf-1.x or tf-2.x), keras, tensorflow.js and tflite models to ONNX via command line or python API. Note: tensorflow.js support was just added. While we tested it with many tfjs models from tfhub, it should be considered experimental. TensorFlow has many more ops than ONNX and occasionally mapping a model to ONNX creates issues. tf2onnx will use the ONNX version installed on your system and installs the latest ONNX version if none is found. We support and test ONNX... -
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Mistral Inference
Official inference library for Mistral models
Open and portable generative AI for devs and businesses. We release open-weight models for everyone to customize and deploy where they want it. Our super-efficient model Mistral Nemo is available under Apache 2.0, while Mistral Large 2 is available through both a free non-commercial license, and a commercial license. -
4
AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference... -
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5
pomegranate
Fast, flexible and easy to use probabilistic modelling in Python
... networks can be dropped into a mixture just as easily as a normal distribution, and hidden Markov models can be dropped into Bayes classifiers to make a classifier over sequences. Together, these two design choices enable a flexibility not seen in any other probabilistic modeling package. -
6
ClearML
Streamline your ML workflow
ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions for thousands of data science teams all over the world. It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments... -
7
Unsloth
Finetune Llama 3.2, Mistral, Phi & Gemma LLMs 2-5x faster
Unsloth is a framework designed to significantly improve the performance of Llama 3.3, DeepSeek-R1, and other reasoning large language models (LLMs). It optimizes these models to run up to 2x faster while using 70% less memory. Unsloth aims to make finetuning large models more efficient, offering users a simple, resource-efficient solution for customizing LLMs with their datasets. It provides a user-friendly experience through free notebooks and the ability to export finetuned models to... -
8
MODMAIL
A feature rich discord Modmail bot
... will automatically relay to that channel; any available staff can respond within the channel. Schedule tasks in human time, e.g. ?close in 2 hours silently. Editing and deleting messages are synced. Support for the diverse range of message contents (multiple images, files). Paginated commands interfaces via reactions. When you close a thread, Modmail will generate a log link and post it to your log channel. -
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openTSNE
Extensible, parallel implementations of t-SNE
openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1], a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2], massive speed improvements [3] [4] [5], enabling t-SNE to scale to millions of data points, and various tricks to improve the global alignment of the resulting... -
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OpenVINO Training Extensions
Trainable models and NN optimization tools
OpenVINO™ Training Extensions provide a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference. When ote_cli is installed in the virtual environment, you can use the ote command line interface to perform various actions for templates related to the chosen task type, such as running, training, evaluating, exporting, etc. ote train trains a model (a particular model template) on a dataset and saves results in two files. ote... -
11
Bard API
The unofficial python package that returns response of Google Bard
The Python package returns a response of Google Bard through the value of the cookie. This package is designed for application to the Python package ExceptNotifier and Co-Coder. Please note that the bardapi is not a free service, but rather a tool provided to assist developers with testing certain functionalities due to the delayed development and release of Google Bard's API. It has been designed with a lightweight structure that can easily adapt to the emergence of an official API. Therefore... -
12
Phenaki - Pytorch
Implementation of Phenaki Video, which uses Mask GIT
Implementation of Phenaki Video, which uses Mask GIT to produce text-guided videos of up to 2 minutes in length, in Pytorch. It will also combine another technique involving a token critic for potentially even better generations. A new paper suggests that instead of relying on the predicted probabilities of each token as a measure of confidence, one can train an extra critic to decide what to iteratively mask during sampling. This repository will also endeavor to allow the researcher to train... -
13
UForm
Multi-Modal Neural Networks for Semantic Search, based on Mid-Fusion
... but often neglect fine-grained ones. This type of models is well-suited for retrieval in large collections. The most famous example of such models is CLIP by OpenAI. Early-fusion models encode both modalities jointly so they can take into account fine-grained features. Usually, these models are used for re-ranking relatively small retrieval results. Mid-fusion models are the golden midpoint between the previous two types. Mid-fusion models consist of two parts – unimodal and multimodal. -
14
llama2-webui
Run any Llama 2 locally with gradio UI on GPU or CPU from anywhere
Running Llama 2 with gradio web UI on GPU or CPU from anywhere (Linux/Windows/Mac). -
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Recurrent Interface Network (RIN)
Implementation of Recurrent Interface Network (RIN)
... for larger images. The big surprise is that the generations can reach this level of fidelity. Will need to verify this on my own machine. Additionally, we will try adding an extra linear attention on the main branch as well as self-conditioning in the pixel space. The insight of being able to self-condition on any hidden state of the network as well as the newly proposed sigmoid noise schedule are the two main findings. -
16
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. -
17
MMEditing
MMEditing is a low-level vision toolbox based on PyTorch
... provides state-of-the-art methods in inpainting/matting/super-resolution/generation. Note that MMSR has been merged into this repo, as a part of MMEditing. With elaborate designs of the new framework and careful implementations, hope MMEditing could provide a better experience. When installing PyTorch in Step 2, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations. -
18
MiniMind
Train a 26M-parameter GPT from scratch in just 2h
minimind is a framework that enables users to train a 26-million-parameter GPT (Generative Pre-trained Transformer) model from scratch in approximately two hours. It provides a streamlined process for data preparation, model training, and evaluation, making it accessible for individuals and organizations to develop their own language models without extensive computational resources. -
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Toloka-Kit
Toloka-Kit is a Python library for working with Toloka API
Toloka-Kit is a Python library for working with Toloka API. The API allows you to build scalable and fully automated human-in-the-loop ML pipelines, and integrate them into your processes. The toolkit makes integration easier. You can use it with Jupyter Notebooks. Support for all common Toloka use cases: creating projects, adding pools, uploading tasks, and so on. Toloka entities are represented as Python classes. You can use them instead of accessing the API using JSON representations... -
20
imodelsX
Interpretable prompting and models for NLP
Interpretable prompting and models for NLP (using large language models). Generates a prompt that explains patterns in data (Official) Explain the difference between two distributions. Find a natural-language prompt using input-gradients. Fit a better linear model using an LLM to extract embeddings. Fit better decision trees using an LLM to expand features. Finetune a single linear layer on top of LLM embeddings. Use these just a like a sci-kit-learn model. During training, they fit better... -
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Haiku
JAX-based neural network library
... DeepMind. It preserves Sonnet’s module-based programming model for state management while retaining access to JAX’s function transformations. Haiku can be expected to compose with other libraries and work well with the rest of JAX. Similar to Sonnet modules, Haiku modules are Python objects that hold references to their own parameters, other modules, and methods that apply functions on user inputs. -
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TensorFlow Privacy
Library for training machine learning models with privacy for data
Library for training machine learning models with privacy for training data. This repository contains the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided. -
23
Haiku Sonnet for JAX
JAX-based neural network library
..., and a simple function transformation, hk.transform. hk.Modules are Python objects that hold references to their own parameters, other modules, and methods that apply functions on user inputs. hk.transform turns functions that use these object-oriented, functionally "impure" modules into pure functions that can be used with jax.jit, jax.grad, jax.pmap, etc. -
24
YYeTsBot
Renren Film and Television bot, fully connected to Renren resources
... a subtitle group. Due to the difference in translations, it is recommended to enter a partial translation and then select from the list. For example, if you want to watch the fourth season of Game of Thrones, just search for "Game of Thrones". Want to keep a resource for yourself, but don't know how to program? It doesn't matter! There are currently two methods available, please choose according to your own situation. -
25
TensorFlow Model Garden
Models and examples built with TensorFlow
... are suitable. A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU).
