Search Results for "morse code decoder"
Sort By:
Showing 16 open source projects for "morse code decoder"
View related business solutions-
Earn up to 16% annual interest with Nexo.Generate interest, access liquidity without selling, and execute trades seamlessly. All in one platform. Geographic restrictions, eligibility, and terms apply.
-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
1
Granite Code Models
A Family of Open Foundation Models for Code Intelligence
Granite Code Models are IBM’s open-source, decoder-only models tailored for code tasks such as fixing bugs, explaining and documenting code, and modernizing codebases. Trained on code from 116 programming languages, the family targets strong performance across diverse benchmarks while remaining accessible to the community. The repository introduces the model lineup, intended uses, and evaluation highlights, and it complements IBM’s broader Granite initiative spanning multiple modalities. ... -
2
Segment Anything
Provides code for running inference with the SegmentAnything Model
Segment Anything (SAM) is a foundation model for image segmentation that’s designed to work “out of the box” on a wide variety of images without task-specific fine-tuning. It’s a promptable segmenter: you guide it with points, boxes, or rough masks, and it predicts high-quality object masks consistent with the prompt. The architecture separates a powerful image encoder from a lightweight mask decoder, so the heavy vision work can be computed once and the interactive part stays fast. A... -
3
LLM Foundry
LLM training code for MosaicML foundation models
Introducing MPT-7B, the first entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available for commercial use, and matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. Large language models (LLMs) are changing the world, but for those outside well-resourced industry labs, it can be extremely difficult to train and deploy... -
4
GLM-OCR
Accurate × Fast × Comprehensive
...The model’s multimodal capabilities allow it to reason across image and text content holistically, capturing structured and unstructured information from pages that include dense tables, seals, code snippets, and varied document graphics. GLM-OCR integrates a comprehensive SDK and inference toolchain that makes it easy for developers to install, invoke, and embed into production pipelines with simple commands or APIs. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
5
CTranslate2
Fast inference engine for Transformer models
CTranslate2 is a C++ and Python library for efficient inference with Transformer models. The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU. The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many... -
6
TimesFM
Pretrained time-series foundation model developed by Google Research
TimesFM is a pretrained time-series foundation model from Google Research built for forecasting tasks, designed to generalize across many domains without requiring extensive per-dataset retraining. It provides a decoder-only model approach to forecasting, aiming for strong performance even in zero-shot or low-data settings where traditional models often struggle. The project includes code and an inference API intended to make it practical to run forecasts programmatically, with options to use different backends such as Torch or Flax depending on your environment and performance needs. ... -
7
OpenAI DALL·E AsyncImage SwiftUI
OpenAI swift async text to image for SwiftUI app using OpenAI
SwiftUI views that asynchronously loads and displays an OpenAI image from open API. You just type in your idea and AI will give you an art solution. DALL-E and DALL-E 2 are deep learning models developed by OpenAI to generate digital images from natural language descriptions, called "prompts". You need to have Xcode 13 installed in order to have access to Documentation Compiler (DocC) OpenAI's text-to-image model DALL-E 2 is a recent example of diffusion models. It uses diffusion models for... -
8
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.... -
9
DeepSeek LLM
DeepSeek LLM: Let there be answers
...The model is trained from scratch, reportedly on a vast multilingual + code + reasoning dataset, and competes with other open or open-weight models. The architecture mirrors established decoder-only transformer families: pre-norm structure, rotational embeddings (RoPE), grouped query attention (GQA), and mixing in languages and tasks. It supports both “Base” (foundation model) and “Chat” (instruction / conversation tuned) variants. -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
10
Basaran
Basaran, an open-source alternative to the OpenAI text completion API
...The open source community will eventually witness the Stable Diffusion moment for large language models (LLMs), and Basaran allows you to replace OpenAI's service with the latest open-source model to power your application without modifying a single line of code. Stream generation using various decoding strategies. Support both decoder-only and encoder-decoder models. Detokenizer that handles surrogates and whitespace. Multi-GPU support with optional 8-bit quantization. Real-time partial progress using server-sent events. Compatible with OpenAI API and client libraries. Comes with a fancy web-based playground. ... -
11
OpenNMT-tf
Neural machine translation and sequence learning using TensorFlow
...While neural machine translation is the main target task, it has been designed to more generally support sequence-to-sequence mapping, sequence tagging, sequence classification, language modeling. Models are described with code to allow training custom architectures and overriding default behavior. For example, the following instance defines a sequence-to-sequence model with 2 concatenated input features, a self-attentional encoder, and an attentional RNN decoder sharing its input and output embeddings. Sequence to sequence models can be trained with guided alignment and alignment information are returned as part of the translation API. -
12
CPT
CPT: A Pre-Trained Unbalanced Transformer
A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation. We replace the old BERT vocabulary with a larger one of size 51271 built from the training data, in which we 1) add missing 6800+ Chinese characters (most of them are traditional Chinese characters); 2) remove redundant tokens (e.g. Chinese character tokens with ## prefix); 3) add some English tokens to reduce OOV. Position Embeddings We extend the max_position_embeddings from 512 to 1024. We... -
13
Mask2Former
Code release for "Masked-attention Mask Transformer
Mask2Former is a unified segmentation architecture that handles semantic, instance, and panoptic segmentation with one model and one training recipe. Its core idea is to cast segmentation as mask classification: a transformer decoder predicts a set of mask queries, each with an associated class score, eliminating the need for task-specific heads. A pixel decoder fuses multi-scale features and feeds masked attention in the transformer so each query focuses computation on its current spatial... -
14
wav2letter++
Facebook AI research's automatic speech recognition toolkit
First, install Flashlight (using the 0.3 branch is required) with the ASR application. This repository includes recipes to reproduce the following research papers as well as pre-trained models. All results reproduction must use Flashlight <= 0.3.2 for exact reproducibility. At least one of LZMA, BZip2, or Z is required for LM compression with KenLM. It is highly recommended to build KenLM with position-independent code (-fPIC) enabled, to enable python compatibility. After installing, run... -
15
DETR
End-to-end object detection with transformers
...It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient. -
16
CakeChat
CakeChat: Emotional Generative Dialog System
CakeChat is a backend for chatbots that are able to express emotions via conversations. The code is flexible and allows to condition model's responses by an arbitrary categorical variable. For example, you can train your own persona-based neural conversational model or create an emotional chatting machine. Hierarchical Recurrent Encoder-Decoder (HRED) architecture for handling deep dialog context. Multilayer RNN with GRU cells.
- Previous
- You're on page 1
- Next
