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  • 1
    Eclipse plugin that allows a user to upload files via protocol(http/ftp/etc) to a server. Allows arbitrary number of files and file size; provides bandwidth management and transmission recovery. Easy to use tool for uploading ordinary files, pictures etc
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  • 2
    The BLOB Streaming engine is a MySQL storage engine that enables the streaming of BLOB data directly in and out of MySQL tables. Using the HTTP protocol, it is possible to "PUT" and "GET" text and media data of any size, to and from a database.
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  • 3

    MoViT

    MoViT: The Mobile Network Virtualized Testbed

    ...MoViT provides researchers and developers with a virtualized environment for developing and testing mobile applications and protocols for any hardware and software platform that can be virtualized. The distributed nature of MoViT allows for the emulation of mobile networks of arbitrary size. Additionally, the network connectivity is shaped transparently such that the connectivity observed by each virtual node resembles that of a physical mobile network.
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  • 4
    Common Lisp Fixed Records Database (FRDB) is an embedded database system used to store records of fixed size. It is best suited to store large volumes of time-series data (as measurements obtained from data acquisition systems, etc.)
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  • Secure File Transfer for Windows with Cerberus by Redwood Icon
    Secure File Transfer for Windows with Cerberus by Redwood

    Protect and share files over FTP/S, SFTP, HTTPS and SCP with the #1 rated Windows file transfer server.

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  • 5

    lrzip

    Long Range Zip

    Lrzip is a lossless compression application designed to scale with modern hardware resources using a variety of different back end compression algorithms with the ability to have unlimited size compression windows and optional high grade encryption. The larger the file the better the compression possible and the more powerful the hardware the faster it can compress with a heavily multithreaded design.
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  • 6
    Mergist

    Mergist

    Mergist is an online tool to combine multiple PDF files into one

    Mergist is an online tool to combine multiple PDF files into one. Mergist has no ads, no file size limits, and your files never leave your device.
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  • 7
    GLM-4.5-Air

    GLM-4.5-Air

    Compact hybrid reasoning language model for intelligent responses

    ...Open-sourced under the MIT license, it is commercially usable and integrates with transformers, vLLM, and SGLang inference frameworks. It includes FP8 variants for faster inference and reduced memory requirements. Despite its smaller size compared to full GLM-4.5, GLM-4.5-Air maintains high performance.
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  • 8
    t5-small

    t5-small

    T5-Small: Lightweight text-to-text transformer for NLP tasks

    ...With only 60 million parameters, T5-Small is compact and suitable for fast inference or deployment in constrained environments. It was pretrained on the C4 dataset using both unsupervised denoising and supervised learning on tasks like sentiment analysis, NLI, and QA. Despite its size, it performs competitively across 24 NLP benchmarks, making it a strong candidate for prototyping and fine-tuning. T5-Small is compatible with major deep learning frameworks including PyTorch, TensorFlow, JAX, and ONNX. The model is open-source under the Apache 2.0 license and has wide support across Hugging Face's ecosystem.
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  • 9
    Llama-3.2-1B

    Llama-3.2-1B

    Llama 3.2–1B: Multilingual, instruction-tuned model for mobile AI

    ...The model supports eight officially listed languages (including Spanish, German, Hindi, and Thai) but can be adapted to more. Llama 3.2-1B outperforms other open models in several benchmarks relative to its size and offers quantized versions for efficiency. It uses a refined transformer architecture with Grouped-Query Attention (GQA) and supports long context windows of up to 128k tokens.
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    Stop vibe-debugging.

    Plug Claude into your app's actual errors.

    AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
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  • 10
    bart-large-cnn

    bart-large-cnn

    Summarization model fine-tuned on CNN/DailyMail articles

    ...Its architecture allows it to model both language understanding and generation tasks effectively. The model supports usage in PyTorch, TensorFlow, and JAX, and is integrated with the Hugging Face pipeline API for simple deployment. Due to its size and performance, it's widely used in real-world summarization applications such as news aggregation, legal document condensing, and content creation.
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  • 11
    bge-base-en-v1.5

    bge-base-en-v1.5

    Efficient English embedding model for semantic search and retrieval

    bge-base-en-v1.5 is an English sentence embedding model from BAAI optimized for dense retrieval tasks, part of the BGE (BAAI General Embedding) family. It is a fine-tuned BERT-based model designed to produce high-quality, semantically meaningful embeddings for tasks like semantic similarity, information retrieval, classification, and clustering. This version (v1.5) improves retrieval performance and stabilizes similarity score distribution without requiring instruction-based prompts. With...
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  • 12

    fsora

    Fast Small Oracle Remote Database Analyzer

    This tool is an add on for remote Oracle Database Analysis based upon a small and powerful script wrapper and hundreds of scripts to analyze nearby everything happens in an Oracle Database. It's an add on and not a replacement for Oracle Cloud Control or Spotlight and other tools. The major benefit is that it is small and can be used by Oracle admins working remote in HomeOffice or standby in 365*24 hour business. The Cloud Control user waits for results you already got.
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  • 13
    DiffusionGemma

    DiffusionGemma

    NVFP4 DiffusionGemma model for fast multimodal text generation

    ...The model supports a 256K-token context window, configurable thinking mode, native function calling, structured JSON output, and multilingual inference across 35+ languages. The NVFP4 quantization reduces weights and activations from 16-bit to 4-bit, lowering disk size and GPU memory needs for vLLM deployment.
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  • 14
    Gemma 4 12B

    Gemma 4 12B

    Unified multimodal Gemma model for local coding and reasoning

    ...The model has 11.95B parameters, 48 layers, a 256K-token context window, and support for over 140 languages. It also includes configurable thinking modes, native system prompt support, function calling, and strong benchmark performance for its size. It is optimized for consumer GPUs, workstations, and streamlined local deployment.
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  • 15
    GigaChat 3 Ultra

    GigaChat 3 Ultra

    High-performance MoE model with MLA, MTP, and multilingual reasoning

    GigaChat 3 Ultra is a flagship instruct-model built on a custom Mixture-of-Experts architecture with 702B total and 36B active parameters. It leverages Multi-head Latent Attention to compress the KV cache into latent vectors, dramatically reducing memory demand and improving inference speed at scale. The model also employs Multi-Token Prediction, enabling multi-step token generation in a single pass for up to 40% faster output through speculative and parallel decoding techniques. Its...
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  • 16
    Jan-v1-edge

    Jan-v1-edge

    Jan-v1-edge: efficient 1.7B reasoning model optimized for edge devices

    Jan-v1-edge is a lightweight agentic language model developed by JanHQ, designed for fast and reliable on-device execution. It is the second release in the Jan Family and was distilled from the larger Jan-v1 model, retaining strong reasoning and problem-solving capabilities while reducing its computational footprint. The model was refined through a two-stage post-training process: Supervised Fine-Tuning (SFT) to transfer knowledge from Jan-v1, followed by Reinforcement Learning with...
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  • 17
    Qwen-Image-Edit

    Qwen-Image-Edit

    An advanced bilingual image editing with semantic control

    ...The model excels at semantic edits like style transfer, object rotation, and novel view synthesis, while also handling precise appearance edits such as adding or removing elements without altering surrounding regions. A standout feature is its bilingual text editing in English and Chinese, which preserves original font, size, and style during modifications. Benchmarks confirm its state-of-the-art performance in image editing, establishing it as a reliable foundation for both artistic and practical tasks. Its applications span IP creation, meme generation, background changes, clothing edits, and fine corrections in artworks or calligraphy.
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  • 18
    Bio_ClinicalBERT

    Bio_ClinicalBERT

    ClinicalBERT model trained on MIMIC notes for clinical NLP tasks

    ...The training focused on improving performance in tasks like named entity recognition and natural language inference within the healthcare domain. Notes were processed using rule-based sectioning and tokenized with SciSpacy. Training was done for 150,000 steps using a batch size of 32, max sequence length of 128, and a masked language modeling objective with a 0.15 mask probability. Bio_ClinicalBERT is available through Hugging Face's Transformers library for easy integration. It supports medical AI research and applications involving electronic health record understanding, clinical decision support, and biomedical information extraction.
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  • 19

    iptraf-ng

    console based network monitoring utility

    ...IPTraf-ng features include an IP traffic monitor which shows TCP flag information, packet and byte counts, ICMP details, OSPF packet types, and oversized IP packet warnings; interface statistics showing IP, TCP, UDP, ICMP, non-IP and other IP packet counts, IP checksum errors, interface activity and packet size counts; a TCP and UDP service monitor showing counts of incoming and outgoing packets for common TCP and UDP application ports, a LAN statistics module that discovers active hosts and displays statistics about their activity; TCP, UDP and other protocol display filters so you can view just the traffic you want; logging; support for Ethernet, FDDI, ISDN, SLIP, PPP, and loopback interfaces.
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  • 20
    Mistral Large 3 675B Base 2512

    Mistral Large 3 675B Base 2512

    Frontier-scale 675B multimodal base model for custom AI training

    Mistral Large 3 675B Base 2512 is the foundational, pre-trained version of the Mistral Large 3 family, built as a frontier-scale multimodal Mixture-of-Experts model with 41B active parameters and a total size of 675B. It is trained from scratch using 3000 H200 GPUs, making it one of the most advanced and compute-intensive open-weight models available. As the base version, it is not fine-tuned for instruction following or reasoning, making it ideal for teams planning their own domain-specific finetuning or custom training pipelines. The model is engineered for reliability, long-context comprehension, and stable performance across many enterprise, scientific, and knowledge-intensive workloads. ...
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  • 21
    Ministral 3 3B Base 2512

    Ministral 3 3B Base 2512

    Small 3B-base multimodal model ideal for custom AI on edge hardware

    ...It supports dozens of languages, making it practical for multilingual, global, or distributed environments. With a large 256k token context window, it can handle long documents, extended inputs, or multi-step processing workflows even at its small size.
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  • 22
    Ministral 3 3B Instruct 2512

    Ministral 3 3B Instruct 2512

    Ultra-efficient 3B multimodal instruct model built for edge deployment

    ...As an FP8 instruct-fine-tuned model, it is optimized for chat, instruction following, and compact agentic tasks while maintaining strong adherence to system prompts. Despite its small size, it delivers efficient real-time performance and can run locally on a single 8GB GPU, with further memory reductions through quantization. It supports dozens of languages across major global regions, making it well-suited for multilingual and embedded applications. The model also provides function calling, clean JSON output, and stable tool-use behavior, enabling it to serve as a small but effective agentic system.
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  • 23
    Ministral 3 14B Instruct 2512

    Ministral 3 14B Instruct 2512

    Efficient 14B multimodal instruct model with edge deployment and FP8

    ...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 adherence. Despite its size, the model is engineered for practical deployment, capable of running locally on a single 24GB GPU when served in FP8 and even less with further quantization. Its multilingual support spans dozens of major languages, making it suitable for global, multilingual, and localized AI applications. The model’s architecture provides native function calling, structured JSON outputs, and reliable tool-use behavior essential for agentic automation. ...
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  • 24

    The Engine

    Game creation engine with built-in BASIC interpreter.

    "The Engine" is a game creation engine with built-in classic type BASIC interpreter (as known from old 8-bit computers), screen editor and full-screen editor, using OpenGL and SDL (currently POSIX only, but portable, in principle). It is aimed at game developers with little spare time, and tries to reduce program size with powerful commands that takes as much coding effort off the shoulders of the developer as humanly possible. The BASIC interpreter supports only labels, not line numbers, and the program is written with the built-in editor. Interpreter and graphics engine run in parallel, in separate threads, and hence interpreter speed is not an issue. ...
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  • 25
    Ministral 3 3B Reasoning 2512

    Ministral 3 3B Reasoning 2512

    Compact 3B-param multimodal model for efficient on-device reasoning

    ...This reasoning-tuned variant is optimized for tasks like math, coding, and other STEM-related problem solving, making it suitable for applications that require logical reasoning, analysis, or structured thinking. Despite its modest size, the model is designed for edge deployment and can run locally, fitting in ~16 GB of VRAM in BF16 or under 8 GB of RAM/VRAM when quantized. It supports dozens of languages, allowing it to function across global and multilingual contexts. The model retains strong system-prompt adherence, supports function-calling with structured JSON output, and offers a large 256k token context window for extended context reasoning.
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