Search Results for "mistral"
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Showing 10 open source projects for "mistral"
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mistral.rs
Fast, flexible LLM inference
mistral.rs is a fast and flexible LLM inference engine implemented in Rust, designed to run and serve modern language models with an emphasis on performance and practical deployment. It provides multiple entry points for developers, including a CLI for running models locally and an HTTP server that exposes an OpenAI-compatible API surface for easy integration with existing clients. The project includes hardware-aware tooling that can benchmark a system and choose sensible quantization and... -
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Jlama
Jlama is a modern LLM inference engine for Java
...This allows organizations to integrate generative AI features into their systems while maintaining full control over data privacy and infrastructure. The engine supports a wide range of open-source model architectures and formats, including variants of Llama, Mistral, and other transformer-based models. It provides tools for running chat interactions, completing prompts, or exposing an OpenAI-compatible REST API for applications that expect standard LLM endpoints. The project focuses on performance and portability by using native Java optimizations and the Java Vector API to accelerate inference workloads. -
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Mirascope
LLM abstractions that aren't obstructions
Mirascope is a powerful, flexible, and user-friendly library that simplifies the process of working with LLMs through a unified interface that works across various supported providers, including OpenAI, Anthropic, Mistral, Gemini, Groq, Cohere, LiteLLM, Azure AI, Vertex AI, and Bedrock. Whether you're generating text, extracting structured information, or developing complex AI-driven agent systems, Mirascope provides the tools you need to streamline your development process and create powerful, robust applications. -
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RunAnywhere
Production ready toolkit to run AI locally
...By running models entirely on device, the platform eliminates network latency and protects user data because information does not leave the device. The SDK supports popular open-source models such as Llama, Mistral, and Qwen, enabling developers to build AI-powered features such as chat interfaces and voice assistants with minimal external dependencies. It also includes integrated pipelines that combine speech-to-text, large language models, and text-to-speech into a complete conversational system. -
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5
How to Train Your GPT
Build a modern LLM from scratch. Every line commented
...It is written for learners with minimal machine-learning background, using simple explanations, commented code, and practical examples. The project covers the same broad family of architecture behind systems such as GPT-style models, LLaMA-style models, Claude-style systems, and Mistral-style models. It includes chapters and topic explainers on tokenizers, embeddings, attention, RoPE, RMSNorm, SwiGLU, KV cache, AdamW, mixed precision, training loops, and inference. The guide emphasizes writing every important component manually rather than only calling high-level APIs. Its purpose is to make the internals of language models understandable through runnable code and step-by-step explanations. -
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RubyLLM
One beautiful Ruby API for OpenAI, Anthropic, Gemini, Bedrock
...The library is designed to simplify the process of integrating AI capabilities into Ruby applications by abstracting away differences between model providers and API formats. Developers can use RubyLLM to communicate with a wide range of AI services including OpenAI, Anthropic, Google Gemini, Mistral, Ollama, and other compatible platforms through a single programming interface. The library supports advanced capabilities such as tool calling, structured responses, and schema-based outputs that enable developers to build more reliable AI-driven applications. RubyLLM also integrates smoothly with modern Ruby frameworks and development workflows, making it easier to embed AI functionality into web services, background jobs, and automation scripts. -
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Intel LLM Library for PyTorch
Accelerate local LLM inference and finetuning
...The framework provides hardware-aware optimizations and low-precision computation techniques that significantly improve the performance of large language models while reducing memory consumption. IPEX-LLM supports a wide range of popular models, including architectures such as LLaMA, Mistral, Qwen, and other transformer-based systems. The library can integrate with common AI frameworks and serving tools such as Hugging Face Transformers, LangChain, and vLLM, allowing developers to incorporate optimized inference into existing pipelines. -
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chatd
Chat with your documents using local AI
...It includes a built-in integration with the Ollama runtime, which provides a cross-platform environment for running large language models locally. The application typically runs models such as Mistral-7B and allows users to load and analyze documents while asking questions in natural language. Unlike many document-chat tools that require manual installation of model servers, chatd packages the model runner with the application so that users can start interacting with documents immediately after launching the program. -
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Secret Llama
Fully private LLM chatbot that runs entirely with a browser
Secret Llama is a privacy-first large-language-model chatbot that runs entirely inside your web browser, meaning no server is required and your conversation data never leaves your device. It focuses on open-source model support, letting you load families like Llama and Mistral directly in the client for fully local inference. Because everything happens in-browser, it can work offline once models are cached, which is helpful for air-gapped environments or travel. The interface mirrors the modern chat UX you’d expect—streaming responses, markdown, and a clean layout—so there’s no usability tradeoff to gain privacy. ... -
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Firefly LLM
A large model training tool that supports training large models
...Its architecture supports both full-parameter training and parameter-efficient strategies like LoRA and QLoRA, making it suitable for environments with limited computational resources. Firefly is compatible with a wide range of popular open-source models including LLaMA, Qwen, Baichuan, InternLM, and Mistral, enabling developers to experiment with different architectures using a consistent training pipeline. The framework also provides curated datasets and training templates that help streamline the process of instruction tuning and conversational model development.
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