Open Source Python Large Language Models (LLM) - Page 5
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Python Large Language Models (LLM)
View 302 business solutionsBrowse free open source Python Large Language Models (LLM) and projects below. Use the toggles on the left to filter open source Python Large Language Models (LLM) by OS, license, language, programming language, and project status.
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GPT Academic
Research-oriented chatbot framework
GPT Academic is a research-oriented chatbot framework designed to integrate large language models (LLMs) into academic workflows. It provides tools for structured document processing, citation management, and enhanced interaction with research papers. -
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GeneralAI
Large-scale Self-supervised Pre-training Across Tasks, Languages, etc.
Fundamental research to develop new architectures for foundation models and AI, focusing on modeling generality and capability, as well as training stability and efficiency. -
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Genoss GPT
One API for all LLMs either Private or Public
One line replacement for openAI ChatGPT & Embeddings powered by OSS models. Genoss is a pioneering open-source initiative that aims to offer a seamless alternative to OpenAI models such as GPT 3.5 & 4, using open-source models like GPT4ALL. -
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Gorilla
Gorilla: An API store for LLMs
Gorilla is Apache 2.0 With Gorilla being fine-tuned on MPT, and Falcon, you can use Gorilla commercially with no obligations. Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them! Hop on our Discord, or open a PR, or email us if you would like to have your API incorporated as well. -
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Gorilla CLI
LLMs for your CLI
Gorilla CLI powers your command-line interactions with a user-centric tool. Simply state your objective, and Gorilla CLI will generate potential commands for execution. Gorilla today supports ~1500 APIs, including Kubernetes, AWS, GCP, Azure, GitHub, Conda, Curl, Sed, and many more. No more recalling intricate CLI arguments. -
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Grade School Math
8.5K high quality grade school math problems
The grade-school-math repository (sometimes called GSM8K) is a curated dataset of 8,500 high-quality grade school math word problems intended for evaluating mathematical reasoning capabilities of language models. It is structured into 7,500 training problems and 1,000 test problems. These aren’t trivial exercises — many require multi-step reasoning, combining arithmetic operations, and handling intermediate steps (e.g. “If she sold half as many in May… how many in total?”). The problems are written by human authors (not automatically generated) to ensure linguistic variety and realism. The repository maintains strict formatting (e.g. JSONL) for problem + answer pairs, and is used broadly in research to benchmark model performance under “word problem” settings. Issues are tracked (people report incorrect problems, ambiguous statements), and contributions are possible for cleaning or expanding the set. -
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GraphRAG
A modular graph-based Retrieval-Augmented Generation (RAG) system
The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs. -
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Guidance
A guidance language for controlling large language models
Guidance is an efficient programming paradigm for steering language models. With Guidance, you can control how output is structured and get high-quality output for your use case—while reducing latency and cost vs. conventional prompting or fine-tuning. It allows users to constrain generation (e.g. with regex and CFGs) as well as to interleave control (conditionals, loops, tool use) and generation seamlessly. -
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ImPromptu
Domain Agnostic Prompts for Savvy Professionals
A community-driven wiki of sorts full of your favorite prompts for various Large Language Models such as ChatGPT, GPT-3, MidJourney, and soon (Google's Bard) and more! Choose a subject area you are interested in, and click the link below to go to the page with prompts for that subject. If that page is empty, then you can help by adding prompts to that page. If you are not sure how to do that, you can read the contributing guidelines. If you are feeling like having your mind melt into magic today then head over to the prompt generator and let the magic happen. This script will literally write your prompts for you, as if chatGPT wasn't enough magic for you already. -
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Infinite Sides
Infinite Craft but in Pyside6 and Python with local LLM
Infinite Craft but in Pyside6 and Python with local LLM (llama2 & others) using Ollama that also lets you create your own crafting game based on any topic Customize the game any way you like in the settings. -
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Instructor Python
Structured outputs for llms
Instructor is a Python library that bridges OpenAI responses with structured data validation using Pydantic models. It lets developers specify expected output schemas and ensures that the responses from OpenAI APIs are automatically parsed and validated against those models. This makes integrating LLMs into structured workflows safer and more predictable, especially in production applications. -
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Kaleidoscope-SDK
User toolkit for analyzing and interfacing with Large Language Models
kaleidoscope-sdk is a Python module used to interact with large language models hosted via the Kaleidoscope service available at: https://github.com/VectorInstitute/kaleidoscope. It provides a simple interface to launch LLMs on an HPC cluster, asking them to perform basic features like text generation, but also retrieve intermediate information from inside the model, such as log probabilities and activations. Users must authenticate using their Vector Institute cluster credentials. This can be done interactively instantiating a client object. This will generate an authentication token that will be used for all subsequent requests. The token will expire after 30 days, at which point the user will be prompted to re-authenticate. -
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Khoj
An AI personal assistant for your digital brain
Get more done with your open-source AI personal assistant. Khoj is a desktop application to search and chat with your notes, documents, and images. It is an offline-first, open-source AI personal assistant that is accessible from Emacs, Obsidian or your Web browser. Khoj is a thinking tool that is transparent, fun, and easy to engage with. You can build faster and better by using Khoj to search and reason across all your data sources. Khoj learns from your notes and documents to function as an extension of your brain. So that you can stay focused on doing what matters. Khoj started with the founding principle that a personal assistant be understandable, accessible and hackable. This means you can always customize and self-host your Khoj on your own machines. -
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Kor
LLM
This is a half-baked prototype that “helps” you extract structured data from text using LLMs. Specify the schema of what should be extracted and provide some examples. Kor will generate a prompt, send it to the specified LLM and parse out the output. You might even get results back. -
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LLM CLI
Access large language models from the command-line
A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine. -
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LLM Foundry
LLM training code for MosaicML foundation models
Introducing MPT-7B, the first entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available for commercial use, and matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. Large language models (LLMs) are changing the world, but for those outside well-resourced industry labs, it can be extremely difficult to train and deploy these models. This has led to a flurry of activity centered on open-source LLMs, such as the LLaMA series from Meta, the Pythia series from EleutherAI, the StableLM series from StabilityAI, and the OpenLLaMA model from Berkeley AI Research. -
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LLaMA
Inference code for Llama models
“Llama” is the repository from Meta (formerly Facebook/Meta Research) containing the inference code for LLaMA (Large Language Model Meta AI) models. It provides utilities to load pre-trained LLaMA model weights, run inference (text generation, chat, completions), and work with tokenizers. Tokenizer utilities, download scripts, shell helpers to fetch model weights with correct licensing/permissions. Includes example scripts for chat completions and text completions to show how to call the models in code. This repo is a core piece of the Llama model infrastructure, used by researchers and developers to run LLaMA models locally or in their infrastructure. It is meant for inference (not training from scratch) and connects with aspects like model cards, responsible use, licensing, etc. -
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LLaMA Efficient Tuning
Easy-to-use LLM fine-tuning framework (LLaMA-2, BLOOM, Falcon
Easy-to-use LLM fine-tuning framework (LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, ChatGLM2) -
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LLaMA Models
Utilities intended for use with Llama models
This repository serves as the central hub for the Llama foundation model family, consolidating model cards, licenses and use policies, and utilities that support inference and fine-tuning across releases. It ties together other stack components (like safety tooling and developer SDKs) and provides canonical references for model variants and their intended usage. The project’s issues and releases reflect an actively used coordination point for the ecosystem, where guidance, utilities, and compatibility notes are published. It complements separate repos that carry code and demos (for example inference kernels or cookbook content) by keeping authoritative metadata and specs here. Model lineages and size variants are documented externally (e.g., Llama 3.x and beyond), with this repo providing the “single source of truth” links and utilities. In practice, teams use llama-models as a reference when selecting variants, aligning licenses, and wiring in helper scripts for deployment. -
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LM Human Preferences
Code for the paper Fine-Tuning Language Models from Human Preferences
lm-human-preferences is the official OpenAI codebase that implements the method from the paper Fine-Tuning Language Models from Human Preferences. Its purpose is to show how to align language models with human judgments by training a reward model from human comparisons and then fine-tuning a policy model using that reward signal. The repository includes scripts to train the reward model (learning to rank or score pairs of outputs), and to fine-tune a policy (a language model) with reinforcement learning (or related techniques) guided by that reward model. The code is provided “as is” and explicitly says it may no longer run out-of-the-box due to dependencies or dataset migrations. It was tested on the smallest GPT-2 (124M parameters) under a specific environment (TensorFlow 1.x, specific CUDA / cuDNN combinations). It includes utilities for launching experiments, sampling from policies, and simple experiment orchestration. -
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LaMDA-pytorch
Open-source pre-training implementation of Google's LaMDA in PyTorch
Open-source pre-training implementation of Google's LaMDA research paper in PyTorch. The totally not sentient AI. This repository will cover the 2B parameter implementation of the pre-training architecture as that is likely what most can afford to train. You can review Google's latest blog post from 2022 which details LaMDA here. You can also view their previous blog post from 2021 on the model. -
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LangChain Apps on Production with Jina
Langchain Apps on Production with Jina & FastAPI
Jina is an open-source framework for building scalable multi-modal AI apps on Production. LangChain is another open-source framework for building applications powered by LLMs. long-chain-serve helps you deploy your LangChain apps on Jina AI Cloud in a matter of seconds. You can benefit from the scalability and serverless architecture of the cloud without sacrificing the ease and convenience of local development. And if you prefer, you can also deploy your LangChain apps on your own infrastructure to ensure data privacy. With long chain-serve, you can craft REST/WebSocket APIs, spin up LLM-powered conversational Slack bots, or wrap your LangChain apps into FastAPI packages on the cloud or on-premises. -
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LangChain-Chatchat
Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge
LangChain-Chatchat (formerly Langchain-ChatGLM): A local knowledge base question answering application implementation based on large language models such as Langchain and ChatGLM. The knowledge base information of the current project is stored in the database, please initialize the database before running the project officially (we strongly recommend that you back up your knowledge files before performing operations). Relying on the open-source LLM and Embedding models supported by this project, this project can realize offline private deployment using all open-source models. At the same time, this project also supports the call of OpenAI GPT API, and will continue to expand the access to various models and model APIs in the future. -
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Langcorn
Serving LangChain LLM apps automagically with FastApi
LangCorn is an API server that enables you to serve LangChain models and pipelines with ease, leveraging the power of FastAPI for a robust and efficient experience. -
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Ling
Ling is a MoE LLM provided and open-sourced by InclusionAI
Ling is a Mixture-of-Experts (MoE) large language model (LLM) provided and open-sourced by inclusionAI. The project offers different sizes (Ling-lite, Ling-plus) and emphasizes flexibility and efficiency: being able to scale, adapt expert activation, and perform across a range of natural language/reasoning tasks. Example scripts, inference pipelines, and documentation. The codebase includes inference, examples, models, documentation, and model download infrastructure. As more developers and researchers engage with the platform, we can expect rapid advancements and improvements, leading to even more sophisticated applications. Model inference and API code (e.g. integration with Transformers). This collaborative approach accelerates development and ensures that the models remain at the forefront of technology, addressing emerging challenges in various fields.
