Showing 6 open source projects for "llm api"

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  • 1
    BricksLLM

    BricksLLM

    Enterprise-grade API gateway that helps you monitor and impose cost

    BricksLLM is an open-source framework for building and managing production-ready LLM (Large Language Model) applications. It provides tooling for prompt engineering, memory management, observability, and chaining, all in one unified developer experience. BricksLLM is designed to reduce boilerplate and increase the maintainability of LLM-based workflows.
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  • 2
    Unsloth-MLX

    Unsloth-MLX

    Bringing the Unsloth experience to Mac users via Apple's MLX framework

    Unsloth-MLX offers developers the power of Unsloth’s efficient large language model fine-tuning experience on Apple Silicon Macs by wrapping Apple’s native MLX framework with an API fully compatible with Unsloth workflows. This project removes traditional barriers that prevent Mac users from prototyping and experimenting with LLM training locally by allowing the same code used in cloud GPU environments to run on M-series hardware, improving workflow continuity and reducing iteration costs. It supports loading and training Hugging Face models with fine-tuning strategies like SFT, DPO, ORPO, and GRPO and even handles exporting models to formats like GGUF for downstream use, although some limitations apply with quantized models. ...
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  • 3
    BentoML

    BentoML

    Unified Model Serving Framework

    BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference...
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  • 4
    FastMCP Framework

    FastMCP Framework

    A TypeScript framework for building MCP servers

    FastMCP is a TypeScript framework aimed at building servers compliant with the Model Context Protocol (MCP), enabling LLMs (large language models) or other clients to access tools, resources, and context through a defined protocol. It allows developers to define “tools” (basically operations or services) and “resources” that can be fetched or interacted with, and supports multiple transport mechanisms (HTTP streaming, SSE, etc) for client-server communication. Because it’s built in...
    Downloads: 1 This Week
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  • 5
    towhee

    towhee

    Framework that is dedicated to making neural data processing

    Towhee is an open-source machine-learning pipeline that helps you encode your unstructured data into embeddings. You can use our Python API to build a prototype of your pipeline and use Towhee to automatically optimize it for production-ready environments. From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to...
    Downloads: 0 This Week
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  • 6
    KotlinDL

    KotlinDL

    High-level Deep Learning Framework written in Kotlin

    KotlinDL is a high-level Deep Learning API written in Kotlin and inspired by Keras. Under the hood, it uses TensorFlow Java API and ONNX Runtime API for Java. KotlinDL offers simple APIs for training deep learning models from scratch, importing existing Keras and ONNX models for inference, and leveraging transfer learning for tailoring existing pre-trained models to your tasks. This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning...
    Downloads: 0 This Week
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