Search Results for "json form" - Page 4
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Showing 93 open source projects for "json form"
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go_spider
An awesome Go concurrent Crawler(spider) framework
...It can be expanded to an Individualized crawler easily or you can use the default crawl components only. Spider gets a Request in Scheduler that has url to be crawled. Then Downloader downloads the result(html, json, jsonp, text) of the Request. The result is saved in Page for parsing in PageProcesser. Html parsing is based on goquery package. Json parsing is based on simple JSON package. Jsonp will converse to json. Text form represents plain text content without a parser. The PageProcesser moduler only parse results. The moduler gets results(key-value pairs) and URLs to be crawled next step. ... -
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bWAPP
an extremely buggy web app !
bWAPP, or a buggy web application, is a free and open source deliberately insecure web application. bWAPP helps security enthusiasts, developers and students to discover and to prevent web vulnerabilities. bWAPP prepares one to conduct successful penetration testing and ethical hacking projects. What makes bWAPP so unique? Well, it has over 100 web bugs! It covers all major known web vulnerabilities, including all risks from the OWASP Top 10 project. The focus is not just on one specific... -
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Jigy Generator Mac OSX
The simplest way to generate a Java Application. It just works!
...Gone are the days of writing complex xml configurations to wire together components and you definitely won't miss managing dependencies or creating DAO's... just fire up Jigy Generator and fill in the form fields and let it construct your project with ease! The resulting project will be a Java web application with a Spring 3.1 Framework configured to run in Netbeans. The project is annotation-based and provides dependency injection of project resources and simple annotation-based transaction management. Your created project will be already configured to connect to your project's database and to return Json responses when required. ... -
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alpaca
Given a web API, Generate client libraries in node, php, python, etc.
API libraries powered and created by Alpaca. Tired of maintaining API libraries in different languages for your website API? This is for you. Do you have an API for your website but no API libraries for whatever reason? This is for you. You are planning to build an API for your website and develop API libraries? This is for you. You define your API according to the format given below, alpaca builds the API libraries along with their documentation. All you have to do is publishing them to... -
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DynForm is acronim from Dynamic Form. DynForm allow to create form uses own metalanguage built on JSON. Main features: inheritance of fields, AJAX, animation, flexible configuration.
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Python library provides the briefest way to construct SQL queries in JSON form. Simple example: ('*','table',id:[1,3]) is Select * from table where id in [1,3]. Sqlalchemy patch module is provided to extend sqlalchemy with convenient jsonSQL meth
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Tpl makes it easy to serialize your C data using just a handful of API functions. The data is stored in its native binary form for maximum efficiency. C, Perl and XML supported. Data is portable across CPU types and OS's from Unix to Mac to Windows.
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The Little Portal Gizmo is a small Java web application container based on Eclipse plug-ins and the Apache HTTP Core. It uses a custom and very light weight API. The web applications you develop for the Gizmo have the form of Eclipse plug-ins.
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My-Bic is a PHP/AJAX framework that aims to be used in enterprise level and below web development projects. My-Bic is capable of handling network down situations, JSON client side encoding, one line form processing and built in visual debugging.
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JSONER is a JavaScript library intented to simplify most used operations with JSON based on event API on JSON tree. It includes: data lookup and binding solution, HTML form by JSON dynamically creation and population, transformation and comparison JSONs.
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Qwen2.5-VL-3B-Instruct
Qwen2.5-VL-3B-Instruct: Multimodal model for chat, vision & video
...As part of the Qwen2.5 series, it supports image-text-to-text generation with capabilities like chart reading, object localization, and structured data extraction. The model can serve as an intelligent visual agent capable of interacting with digital interfaces and understanding long-form videos by dynamically sampling resolution and frame rate. It uses a SwiGLU and RMSNorm-enhanced ViT architecture and introduces mRoPE updates for robust temporal and spatial understanding. The model supports flexible image input (file path, URL, base64) and outputs structured responses like bounding boxes or JSON, making it highly versatile in commercial and research settings. ... -
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Qwen2.5-14B-Instruct
Powerful 14B LLM with strong instruction and long-text handling
...It’s resilient to varied prompt styles and is especially effective for JSON and tabular data generation. The model is instruction-tuned and supports chat templating, making it ideal for chatbot and assistant use cases. -
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Ministral 3 8B Reasoning 2512
Efficient 8B multimodal model tuned for advanced reasoning tasks.
Ministral 3 8B Reasoning 2512 is a balanced midsize model in the Ministral 3 family, delivering strong multimodal reasoning capabilities within an efficient footprint. It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling it to process both text and images for advanced reasoning tasks. This version is specifically post-trained for reasoning, making it well-suited for math, coding, and STEM applications requiring multi-step logic and problem-solving. Despite its... -
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Ministral 3 14B Reasoning 2512
High-precision 14B multimodal model built for advanced reasoning tasks
...Despite its scale, the model is engineered for practical deployment and can run locally on 32GB of VRAM in BF16 or under 24GB when quantized. It maintains robust system-prompt adherence, supports dozens of languages, and provides native function calling with clean JSON output for agentic workflows. The model's architecture also delivers a 256k context window, unlocking large-document analysis and long-form reasoning. -
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Ministral 3 14B Instruct 2512
Efficient 14B multimodal instruct model with edge deployment and FP8
Ministral 3 14B Instruct 2512 is the largest model in the Ministral 3 family, delivering frontier performance comparable to much larger systems while remaining optimized for edge-level deployment. It combines a 13.5B-parameter language model with a 0.4B-parameter vision encoder, enabling strong multimodal understanding in both text and image tasks. This FP8 instruct-tuned variant is designed specifically for chat, instruction following, and agentic workflows with robust system-prompt... -
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Ministral 3 3B Reasoning 2512
Compact 3B-param multimodal model for efficient on-device reasoning
Ministral 3 3B Reasoning 2512 is the smallest reasoning-capable model in the Ministal-3 family, yet delivers a surprisingly capable multimodal and multilingual base for lightweight AI applications. It pairs a 3.4B-parameter language model with a 0.4B-parameter vision encoder, enabling it to understand both text and image inputs. This reasoning-tuned variant is optimized for tasks like math, coding, and other STEM-related problem solving, making it suitable for applications that require... -
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QwQ-32B
QwQ-32B is a reasoning-focused language model for complex tasks
QwQ-32B is a 32.8 billion parameter reasoning-optimized language model developed by Qwen as part of the Qwen2.5 family, designed to outperform conventional instruction-tuned models on complex tasks. Built with RoPE positional encoding, SwiGLU activations, RMSNorm, and Attention QKV bias, it excels in multi-turn conversation and long-form reasoning. It supports an extended context length of up to 131,072 tokens and incorporates supervised fine-tuning and reinforcement learning for enhanced... -
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Qwen2.5-VL-7B-Instruct
Multimodal 7B model for image, video, and text understanding tasks
Qwen2.5-VL-7B-Instruct is a multimodal vision-language model developed by the Qwen team, designed to handle text, images, and long videos with high precision. Fine-tuned from Qwen2.5-VL, this 7-billion-parameter model can interpret visual content such as charts, documents, and user interfaces, as well as recognize common objects. It supports complex tasks like visual question answering, localization with bounding boxes, and structured output generation from documents. The model is also...
