Search Results for "llama-2-7b"
Sort By:
Showing 572 open source projects for "llama-2-7b"
View related business solutions-
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
1
Llama Stack
Composable building blocks to build Llama Apps
Llama-Stack is an open-source framework designed to facilitate the deployment and fine-tuning of large language models (LLMs) for various natural language processing tasks. -
2
Llama Cloud Services
Knowledge Agents and Management in the Cloud
Llama Cloud Services is a suite of tools designed to facilitate the integration of large language models (LLMs) into applications. It offers components for parsing, extracting, and reporting on complex documents, streamlining the process of preparing data for LLM consumption. -
3
Django Two-Factor Authentication
Complete Two-Factor Authentication for Django
Complete Two-Factor Authentication for Django. Built on top of the one-time password framework django-otp and Django's built-in authentication framework django.contrib.auth for providing the easiest integration into most Django projects. Inspired by the user experience of Google's Two-Step Authentication, allowing users to authenticate through call, text messages (SMS), by using a token generator app like Google Authenticator or a YubiKey hardware token generator (optional). ... -
4
tinygrad
Deep learning framework
This may not be the best deep learning framework, but it is a deep learning framework. Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. If XLA is CISC, tinygrad is RISC. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
-
5
OnlineJudge 2.0
Open source online judge based on Vue, Django and Docker
An open-source online judge system based on Python and Vue. Based on Docker; One-click deployment. Separated backend and frontend; Modular programming; Micro service. ACM/OI rule support; realtime/non-realtime rank support. Amazing charting and visualization. Template-problem support. More reasonable permission control. Multi-language support: C, C++, Java, Python2, Python3. Markdown & MathJax support. Contest participants IP limit(CIDR). You can control the menu and chart status in rankings. -
6
JAX Toolbox
Public CI, Docker images for popular JAX libraries
...It provides prebuilt Docker images, continuous integration pipelines, and optimized example implementations that help developers quickly set up and run JAX workloads without complex configuration. The project supports popular JAX-based frameworks and models, including architectures used for large-scale pretraining such as GPT and LLaMA variants. By offering curated environments and tested configurations, it reduces compatibility issues and accelerates development workflows for both research and production. The repository also includes performance-optimized examples that demonstrate best practices for leveraging NVIDIA hardware effectively. Its integration with container-based workflows makes it suitable for reproducible experiments and scalable deployments across different environments. -
7
Penzai
A JAX research toolkit to build, edit, & visualize neural networks
Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained... -
8
Synthetic Data Kit
Tool for generating high quality Synthetic datasets
Synthetic Data Kit is a CLI-centric toolkit for generating high-quality synthetic datasets to fine-tune Llama models, with an emphasis on producing reasoning traces and QA pairs that line up with modern instruction-tuning formats. It ships an opinionated, modular workflow that covers ingesting heterogeneous sources (documents, transcripts), prompting models to create labeled examples, and exporting to fine-tuning schemas with minimal glue code. -
9
Kivy
Innovative user interfaces made easy
Kivy is an open source, cross-platform UI framework that lets you develop applications that make use of innovative, multi-touch user interfaces. Written in Python with a graphics engine built over OpenGL ES 2, Kivy supports various input devices and protocols, and gives you access to over 20 widgets that are all highly extensible and have built-in multi-touch support. You can run the same codebase on Mac, Windows, Linux, Android and iOS. Kivy is 100% free and open source with a professionally developed and used toolkit, as well as a stable framework and well-documented API, so you can be confident in using it in a commercial product. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
10
TensorFlow Model Garden
Models and examples built with TensorFlow
...To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models 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). -
11
DocTR
Library for OCR-related tasks powered by Deep Learning
...End-to-End OCR is achieved in docTR using a two-stage approach: text detection (localizing words), then text recognition (identify all characters in the word). As such, you can select the architecture used for text detection, and the one for text recognition from the list of available implementations. -
12
GHunt
Offensive Google framework
...It's currently focused on OSINT, but any use related with Google is possible. It will automatically use venvs to avoid dependency conflicts with other projects. First, launch the listener by doing ghunt login and choose between 1 of the 2 first methods. Put GHunt on listening mode (currently not compatible with docker) Paste base64-encoded cookies. Enter manually all cookies. The development of this extension has followed Firefox guidelines to use the Promise-based WebExtension/BrowserExt API being standardized by the W3 Browser Extensions group, and is using webextension-polyfill to provide cross-browser compatibility with no changes. -
13
Pylint
It's not just a linter that annoys you!
Pylint is a static code analyzer for Python 2 or 3. The latest version supports Python 3.7.2 and above. Pylint analyses your code without actually running it. It checks for errors, enforces a coding standard, looks for code smells, and can make suggestions about how the code could be refactored. Projects that you might want to use alongside pylint include flake8 (faster and simpler checks with very few false positives), mypy, pyright or pyre (typing checks), bandit (security-oriented checks), black and isort (auto-formatting), autoflake (automated removal of unused import or variable), pyupgrade (automated upgrade to newer python syntax) and pydocstringformatter (automated pep257). ... -
14
pywebview
Build GUI for your Python program with JavaScript, HTML, and CSS
...It gives you power of web technologies in your desktop application, hiding the fact that GUI is browser based. You can use pywebview either with a lightweight web framework like Flask or Bottle or on its own with a two way bridge between Python and DOM. pywebview uses native GUI for creating a web component window: WinForms on Windows, Cocoa on macOS and QT or GTK on Linux. If you choose to freeze your application, pywebview does not bundle a heavy GUI toolkit or web renderer with it keeping the executable size small. pywebview is compatible with Python 3. -
15
Qiskit
Qiskit is an open-source SDK for working with quantum computers
Qiskit [kiss-kit] is an open-source SDK for working with quantum computers at the level of pulses, circuits, and application modules. When you are looking to start Qiskit, you have two options. You can start Qiskit locally, which is much more secure and private, or you get started with Jupyter Notebooks hosted in IBM Quantum Lab. Qiskit includes a comprehensive set of quantum gates and a variety of pre-built circuits so users at all levels can use Qiskit for research and application development. The transpiler translates Qiskit code into an optimized circuit using a backend’s native gate set, allowing users to program for any quantum processor or processor architecture with minimal inputs. ... -
16
virtualenv
Virtual Python environment builder
...The basic problem being addressed is one of dependencies and versions, and indirectly permissions. Imagine you have an application that needs version 1 of LibFoo, but another application requires version 2. How can you use both these libraries? If you install everything into your host python (e.g. python3.8) it’s easy to end up in a situation where two packages have conflicting requirements. -
17
CO3D (Common Objects in 3D)
Tooling for the Common Objects In 3D dataset
CO3Dv2 (Common Objects in 3D, version 2) is a large-scale 3D computer vision dataset and toolkit from Facebook Research designed for training and evaluating category-level 3D reconstruction methods using real-world data. It builds upon the original CO3Dv1 dataset, expanding both scale and quality—featuring 2× more sequences and 4× more frames, with improved image fidelity, more accurate segmentation masks, and enhanced annotations for object-centric 3D reconstruction. ... -
18
TextDistance
Compute distance between sequences
Python library for comparing the distance between two or more sequences by many algorithms. For main algorithms, text distance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). Install text distance with extras for this feature. Textdistance use benchmark results for algorithm optimization and try to call the fastest external lib first (if possible). -
19
NetworkX
Network analysis in Python
...Well tested with over 90% code coverage. Additional benefits from Python include fast prototyping, easy to teach, and multi-platform. Find the shortest path between two nodes in an undirected graph. Python’s None object is not allowed to be used as a node. It determines whether optional function arguments have been assigned in many functions. And it can be used as a sentinel object meaning “not a node”. -
20
AWS IoT Device SDK for Python
SDK for connecting to AWS IoT from a device using Python
...It is a complete rework, built to improve reliability, performance, and security. We invite your feedback! The SDK is built on top of a modified Paho MQTT Python client library. Developers can choose from two types of connections to connect to AWS IoT. For MQTT over TLS (port 8883 and port 443), a valid certificate and a private key are required for authentication. For MQTT over the WebSocket protocol (port 443), a valid AWS Identity and Access Management (IAM) access key ID and secret access key pair are required for authentication. -
21
Flagsmith
Open source feature flagging and remote config service
...Manage feature flags by the development environment, and for individual users, a segment of users, or a percentage. This means quickly implementing practices like canary deployments. Multivariate flags allow you to use a percentage split across two or more variations for precise A/B/n testing and experimentation. -
22
Python Progressbar
Progressbar 2 - A progress bar for Python 2 and Python 3
A text progress bar is typically used to display the progress of a long-running operation, providing a visual cue that processing is underway. The progressbar is based on the old Python progressbar package that was published on the now-defunct Google Code. Since that project was completely abandoned by its developer and the developer did not respond to my email, I decided to fork the package. This package is still backward compatible with the original progressbar package so you can safely... -
23
Python colorlog
A colored formatter for the python logging module
Add colors to the output of Python's logging module. This library is over a decade old and supported a wide set of Python versions for most of its life, which has made it a difficult library to add new features to. colorlog 6 may break backward compatibility so that newer features can be added more easily, but may still not accept all changes or feature requests. colorlog 4 might accept essential bug fixes but should not be considered actively maintained and will not accept any major changes... -
24
Mobly
E2E test framework for tests with complex environment requirements
Mobly is a Python-based test framework that specializes in supporting test cases that require multiple devices, complex environments, or custom hardware setups. P2P data transfer between two devices. Conference calls across three phones. Wearable device interacting with a phone. Internet-Of-Things devices interacting with each other. Testing RF characteristics of devices with special equipment. Testing LTE network by controlling phones, base stations, and eNBs. Mobly can support many different types of devices and equipment, and it's easy to plug your own device or custom equipment/service into Mobly. ... -
25
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, transforms etc. ...
