Open Source Python Large Language Models (LLM) - Page 5
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Python Large Language Models (LLM)
View 320 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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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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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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HumanEval
Code for the paper "Evaluating Large Language Models Trained on Code"
human-eval is a benchmark dataset and evaluation framework created by OpenAI for measuring the ability of language models to generate correct code. It consists of hand-written programming problems with unit tests, designed to assess functional correctness rather than superficial metrics like text similarity. Each task includes a natural language prompt and a function signature, requiring the model to generate an implementation that passes all provided tests. The benchmark has become a standard for evaluating code generation models, including those in the Codex and GPT families. Researchers can use the dataset to run reproducible comparisons across models and track improvements in functional code synthesis. By focusing on correctness through execution, human-eval provides a rigorous and practical way to evaluate programming capabilities in AI systems. -
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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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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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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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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 3
The official Meta Llama 3 GitHub site
This repository is the former home for Llama 3 model artifacts and getting-started code, covering pre-trained and instruction-tuned variants across multiple parameter sizes. It introduced the public packaging of weights, licenses, and quickstart examples that helped developers fine-tune or run the models locally and on common serving stacks. As the Llama stack evolved, Meta consolidated repositories and marked this one deprecated, pointing users to newer, centralized hubs for models, utilities, and docs. Even as a deprecated repo, it documents the transition path and preserves references that clarify how Llama 3 releases map into the current ecosystem. Practically, it functioned as a bridge between Llama 2 and later Llama releases by standardizing distribution and starter code for inference and fine-tuning. Teams still treat it as historical reference material for version lineage and migration notes. -
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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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LangCheck
Simple, Pythonic building blocks to evaluate LLM applications
Simple, Pythonic building blocks to evaluate LLM applications. -
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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. -
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning. -
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MGIE
Guiding Instruction-based Image Editing via Multimodal Large Language
MGIE—Guiding Instruction-based Image Editing—demonstrates how a multimodal LLM can parse natural-language editing instructions and then drive image transformations accordingly. The project focuses on making edits explainable and controllable: the model interprets text guidance, reasons over image content, and outputs edits aligned with user intent. It’s positioned as an ICLR 2024 Spotlight work, with code and references that show how to connect language planning to concrete image operations. This bridges a gap between free-form prompts and precise edits by letting users describe “what” and “where” in everyday language. The repo includes instructions, examples, and links that situate MGIE within Apple’s broader line of multimodal research. For practitioners, MGIE provides a blueprint for text-to-edit systems that are more semantically grounded than naive prompt-only pipelines. -
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ML Ferret
Refer and Ground Anything Anywhere at Any Granularity
Ferret is Apple’s end-to-end multimodal large language model designed specifically for flexible referring and grounding: it can understand references of any granularity (boxes, points, free-form regions) and then ground open-vocabulary descriptions back onto the image. The core idea is a hybrid region representation that mixes discrete coordinates with continuous visual features, so the model can fluidly handle “any-form” referring while maintaining precise spatial localization. The repo presents the vision-language pipeline, model assets, and paper resources that show how Ferret answers questions, follows instructions, and returns grounded outputs rather than just text. In practice, this enables tasks like “find that small red icon next to the chart and describe it” where both the linguistic reference and the visual region are ambiguous without fine spatial reasoning. -
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MedicalGPT
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training
MedicalGPT training medical GPT model with ChatGPT training pipeline, implementation of Pretraining, Supervised Finetuning, Reward Modeling and Reinforcement Learning. MedicalGPT trains large medical models, including secondary pre-training, supervised fine-tuning, reward modeling, and reinforcement learning training. -
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MegaParse
File Parser optimised for LLM Ingestion with no loss
MegaParse is a file parser optimized for Large Language Model (LLM) ingestion, ensuring no loss of information. It efficiently parses various document formats, such as PDFs, DOCX, and PPTX, converting them into formats ideal for processing by LLMs. This tool is essential for applications that require accurate and comprehensive data extraction from diverse document types. -
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MiniMax-01
Large-language-model & vision-language-model based on Linear Attention
MiniMax-01 is the official repository for two flagship models: MiniMax-Text-01, a long-context language model, and MiniMax-VL-01, a vision-language model built on top of it. MiniMax-Text-01 uses a hybrid attention architecture that blends Lightning Attention, standard softmax attention, and Mixture-of-Experts (MoE) routing to achieve both high throughput and long-context reasoning. It has 456 billion total parameters with 45.9 billion activated per token and is trained with advanced parallel strategies such as LASP+, varlen ring attention, and Expert Tensor Parallelism, enabling a training context of 1 million tokens and up to 4 million tokens at inference. MiniMax-VL-01 extends this core by adding a 303M-parameter Vision Transformer and a two-layer MLP projector in a ViT–MLP–LLM framework, allowing the model to process images at dynamic resolutions up to 2016×2016. -
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MiniMind
Train a 26M-parameter GPT from scratch in just 2h
minimind is a framework that enables users to train a 26-million-parameter GPT (Generative Pre-trained Transformer) model from scratch in approximately two hours. It provides a streamlined process for data preparation, model training, and evaluation, making it accessible for individuals and organizations to develop their own language models without extensive computational resources.
