llm-github-models 0.14

LLM plugin to access GitHub Models API

GitHub Models Plugin for LLM

This is a plugin for llm that uses GitHub Models via the Azure AI Inference SDK.

Installation

$ llm install llm-github-models

Usage

To set the API key, use the llm keys set github command or use the GITHUB_MODELS_KEY environment variable.

To get an API key, create a personal access token (PAT) inside GitHub Settings.

Learn about rate limits here

All model names are affixed with github/ to distinguish the OpenAI ones from the builtin models.

Example

$ llm prompt 'top facts about cheese' -m github/gpt-4.1-mini
Sure! Here are some top facts about cheese:

1. **Ancient Origins**: Cheese is one of the oldest man-made foods, with evidence of cheese-making dating back over 7,000 years.

2. **Variety**: There are over 1,800 distinct types of cheese worldwide, varying by texture, flavor, milk source, and production methods.

Image attachments

Multi-modal vision models can accept image attachments using the LLM attachments options:

llm -m github/Llama-3.2-11B-Vision-Instruct "Describe this image" -a https://static.simonwillison.net/static/2024/pelicans.jpg

Produces

This image depicts a dense gathering of pelicans, with the largest birds situated in the center, showcasing their light brown plumage and long, pointed beaks. The pelicans are standing on a rocky shoreline, with a serene body of water behind them, characterized by its pale blue hue and gentle ripples. In the background, a dark, rocky cliff rises, adding depth to the scene.

The overall atmosphere of the image exudes tranquility, with the pelicans seemingly engaging in a social gathering or feeding activity. The photograph's clarity and focus on the pelicans' behavior evoke a sense of observation and appreciation for the natural world.

Supported Models

AI21 Jamba 1.5 Large

Usage: llm -m github/AI21-Jamba-1.5-Large

Publisher: AI21 Labs

Description: A 398B parameters (94B active) multilingual model, offering a 256K long context window, function calling, structured output, and grounded generation.

AI21 Jamba 1.5 Mini

Usage: llm -m github/AI21-Jamba-1.5-Mini

Publisher: AI21 Labs

Description: A 52B parameters (12B active) multilingual model, offering a 256K long context window, function calling, structured output, and grounded generation.

Aurora

Usage: llm -m github/Aurora

Publisher: Microsoft

Description: Aurora is a machine learning model that can predict general environmental variables.

Codestral 25.01

Usage: llm -m github/Codestral-2501

Publisher: Mistral AI

Description: Codestral 25.01 by Mistral AI is designed for code generation, supporting 80+ programming languages, and optimized for tasks like code completion and fill-in-the-middle

Cohere Command R

Usage: llm -m github/Cohere-command-r

Publisher: Cohere

Description: Command R is a scalable generative model targeting RAG and Tool Use to enable production-scale AI for enterprise.

Cohere Command R 08-2024

Usage: llm -m github/Cohere-command-r-08-2024

Publisher: Cohere

Description: Command R is a scalable generative model targeting RAG and Tool Use to enable production-scale AI for enterprise.

Cohere Command R+

Usage: llm -m github/Cohere-command-r-plus

Publisher: Cohere

Description: Command R+ is a state-of-the-art RAG-optimized model designed to tackle enterprise-grade workloads.

Cohere Command R+ 08-2024

Usage: llm -m github/Cohere-command-r-plus-08-2024

Publisher: Cohere

Description: Command R+ is a state-of-the-art RAG-optimized model designed to tackle enterprise-grade workloads.

Cohere Embed v3 English

Usage: llm -m github/Cohere-embed-v3-english

Publisher: Cohere

Description: Cohere Embed English is the market's leading text representation model used for semantic search, retrieval-augmented generation (RAG), classification, and clustering.

Cohere Embed v3 Multilingual

Usage: llm -m github/Cohere-embed-v3-multilingual

Publisher: Cohere

Description: Cohere Embed Multilingual is the market's leading text representation model used for semantic search, retrieval-augmented generation (RAG), classification, and clustering.

DeepSeek-R1

Usage: llm -m github/DeepSeek-R1

Publisher: DeepSeek

Description: DeepSeek-R1 excels at reasoning tasks using a step-by-step training process, such as language, scientific reasoning, and coding tasks.

DeepSeek-V3

Usage: llm -m github/DeepSeek-V3

Publisher: DeepSeek

Description: A strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.

DeepSeek-V3-0324

Usage: llm -m github/DeepSeek-V3-0324

Publisher: DeepSeek

Description: DeepSeek-V3-0324 demonstrates notable improvements over its predecessor, DeepSeek-V3, in several key aspects, including enhanced reasoning, improved function calling, and superior code generation capabilities.

Llama-3.2-11B-Vision-Instruct

Usage: llm -m github/Llama-3.2-11B-Vision-Instruct

Publisher: Meta

Description: Excels in image reasoning capabilities on high-res images for visual understanding apps.

Llama-3.2-90B-Vision-Instruct

Usage: llm -m github/Llama-3.2-90B-Vision-Instruct

Publisher: Meta

Description: Advanced image reasoning capabilities for visual understanding agentic apps.

Llama-3.3-70B-Instruct

Usage: llm -m github/Llama-3.3-70B-Instruct

Publisher: Meta

Description: Llama 3.3 70B Instruct offers enhanced reasoning, math, and instruction following with performance comparable to Llama 3.1 405B.

Llama 4 Maverick 17B 128E Instruct FP8

Usage: llm -m github/Llama-4-Maverick-17B-128E-Instruct-FP8

Publisher: Meta

Description: Llama 4 Maverick 17B 128E Instruct FP8 is great at precise image understanding and creative writing, offering high quality at a lower price compared to Llama 3.3 70B

Llama 4 Scout 17B 16E Instruct

Usage: llm -m github/Llama-4-Scout-17B-16E-Instruct

Publisher: Meta

Description: Llama 4 Scout 17B 16E Instruct is great at multi-document summarization, parsing extensive user activity for personalized tasks, and reasoning over vast codebases.

MAI-DS-R1

Usage: llm -m github/MAI-DS-R1

Publisher: Microsoft

Description: MAI-DS-R1 is a DeepSeek-R1 reasoning model that has been post-trained by the Microsoft AI team to fill in information gaps in the previous version of the model and improve its harm protections while maintaining R1 reasoning capabilities.

Meta-Llama-3-70B-Instruct

Usage: llm -m github/Meta-Llama-3-70B-Instruct

Publisher: Meta

Description: A powerful 70-billion parameter model excelling in reasoning, coding, and broad language applications.

Meta-Llama-3-8B-Instruct

Usage: llm -m github/Meta-Llama-3-8B-Instruct

Publisher: Meta

Description: A versatile 8-billion parameter model optimized for dialogue and text generation tasks.

Meta-Llama-3.1-405B-Instruct

Usage: llm -m github/Meta-Llama-3.1-405B-Instruct

Publisher: Meta

Description: The Llama 3.1 instruction tuned text only models are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

Meta-Llama-3.1-70B-Instruct

Usage: llm -m github/Meta-Llama-3.1-70B-Instruct

Publisher: Meta

Description: The Llama 3.1 instruction tuned text only models are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

Meta-Llama-3.1-8B-Instruct

Usage: llm -m github/Meta-Llama-3.1-8B-Instruct

Publisher: Meta

Description: The Llama 3.1 instruction tuned text only models are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

Ministral 3B

Usage: llm -m github/Ministral-3B

Publisher: Mistral AI

Description: Ministral 3B is a state-of-the-art Small Language Model (SLM) optimized for edge computing and on-device applications. As it is designed for low-latency and compute-efficient inference, it it also the perfect model for standard GenAI applications that have

Mistral Large 24.11

Usage: llm -m github/Mistral-Large-2411

Publisher: Mistral AI

Description: Mistral Large 24.11 offers enhanced system prompts, advanced reasoning and function calling capabilities.

Mistral Nemo

Usage: llm -m github/Mistral-Nemo

Publisher: Mistral AI

Description: Mistral Nemo is a cutting-edge Language Model (LLM) boasting state-of-the-art reasoning, world knowledge, and coding capabilities within its size category.

Mistral Large

Usage: llm -m github/Mistral-large

Publisher: Mistral AI

Description: Mistral's flagship model that's ideal for complex tasks that require large reasoning capabilities or are highly specialized (Synthetic Text Generation, Code Generation, RAG, or Agents).

Mistral Large (2407)

Usage: llm -m github/Mistral-large-2407

Publisher: Mistral AI

Description: Mistral Large (2407) is an advanced Large Language Model (LLM) with state-of-the-art reasoning, knowledge and coding capabilities.

Mistral Small

Usage: llm -m github/Mistral-small

Publisher: Mistral AI

Description: Mistral Small can be used on any language-based task that requires high efficiency and low latency.

Muse

Usage: llm -m github/Muse

Publisher: Microsoft

Description: Muse is a World and Human Action Model (WHAM), a generative model of gameplay (visuals and/or controller actions).

Phi-3-medium instruct (128k)

Usage: llm -m github/Phi-3-medium-128k-instruct

Publisher: Microsoft

Description: Same Phi-3-medium model, but with a larger context size for RAG or few shot prompting.

Phi-3-medium instruct (4k)

Usage: llm -m github/Phi-3-medium-4k-instruct

Publisher: Microsoft

Description: A 14B parameters model, proves better quality than Phi-3-mini, with a focus on high-quality, reasoning-dense data.

Phi-3-mini instruct (128k)

Usage: llm -m github/Phi-3-mini-128k-instruct

Publisher: Microsoft

Description: Same Phi-3-mini model, but with a larger context size for RAG or few shot prompting.

Phi-3-mini instruct (4k)

Usage: llm -m github/Phi-3-mini-4k-instruct

Publisher: Microsoft

Description: Tiniest member of the Phi-3 family. Optimized for both quality and low latency.

Phi-3-small instruct (128k)

Usage: llm -m github/Phi-3-small-128k-instruct

Publisher: Microsoft

Description: Same Phi-3-small model, but with a larger context size for RAG or few shot prompting.

Phi-3-small instruct (8k)

Usage: llm -m github/Phi-3-small-8k-instruct

Publisher: Microsoft

Description: A 7B parameters model, proves better quality than Phi-3-mini, with a focus on high-quality, reasoning-dense data.

Phi-3.5-MoE instruct (128k)

Usage: llm -m github/Phi-3.5-MoE-instruct

Publisher: Microsoft

Description: A new mixture of experts model

Phi-3.5-mini instruct (128k)

Usage: llm -m github/Phi-3.5-mini-instruct

Publisher: Microsoft

Description: Refresh of Phi-3-mini model.

Phi-3.5-vision instruct (128k)

Usage: llm -m github/Phi-3.5-vision-instruct

Publisher: Microsoft

Description: Refresh of Phi-3-vision model.

Phi-4

Usage: llm -m github/Phi-4

Publisher: Microsoft

Description: Phi-4 14B, a highly capable model for low latency scenarios.

Phi-4-mini-instruct

Usage: llm -m github/Phi-4-mini-instruct

Publisher: Microsoft

Description: 3.8B parameters Small Language Model outperforming larger models in reasoning, math, coding, and function-calling

Phi-4-mini-reasoning

Usage: llm -m github/Phi-4-mini-reasoning

Publisher: Microsoft

Description: Lightweight math reasoning model optimized for multi-step problem solving

Phi-4-multimodal-instruct

Usage: llm -m github/Phi-4-multimodal-instruct

Publisher: Microsoft

Description: First small multimodal model to have 3 modality inputs (text, audio, image), excelling in quality and efficiency

Phi-4-Reasoning

Usage: llm -m github/Phi-4-reasoning

Publisher: Microsoft

Description: State-of-the-art open-weight reasoning model.

Cohere Command A

Usage: llm -m github/cohere-command-a

Publisher: Cohere

Description: Command A is a highly efficient generative model that excels at agentic and multilingual use cases.

Cohere Embed 4

Usage: llm -m github/embed-v-4-0

Publisher: Cohere

Description: Embed 4 transforms texts and images into numerical vectors

OpenAI GPT-4.1

Usage: llm -m github/gpt-4.1

Publisher: OpenAI

Description: gpt-4.1 outperforms gpt-4o across the board, with major gains in coding, instruction following, and long-context understanding

OpenAI GPT-4.1-mini

Usage: llm -m github/gpt-4.1-mini

Publisher: OpenAI

Description: gpt-4.1-mini outperform gpt-4o-mini across the board, with major gains in coding, instruction following, and long-context handling

OpenAI GPT-4.1-nano

Usage: llm -m github/gpt-4.1-nano

Publisher: OpenAI

Description: gpt-4.1-nano provides gains in coding, instruction following, and long-context handling along with lower latency and cost

OpenAI GPT-4o

Usage: llm -m github/gpt-4o

Publisher: OpenAI

Description: OpenAI's most advanced multimodal model in the gpt-4o family. Can handle both text and image inputs.

OpenAI GPT-4o mini

Usage: llm -m github/gpt-4o-mini

Publisher: OpenAI

Description: An affordable, efficient AI solution for diverse text and image tasks.

Grok 3

Usage: llm -m github/grok-3

Publisher: xAI

Description: Grok 3 is xAI's debut model, pretrained by Colossus at supermassive scale to excel in specialized domains like finance, healthcare, and the law.

Grok 3 Mini

Usage: llm -m github/grok-3-mini

Publisher: xAI

Description: Grok 3 Mini is a lightweight model that thinks before responding. Trained on mathematic and scientific problems, it is great for logic-based tasks.

JAIS 30b Chat

Usage: llm -m github/jais-30b-chat

Publisher: Core42

Description: JAIS 30b Chat is an auto-regressive bilingual LLM for Arabic & English with state-of-the-art capabilities in Arabic.

Mistral Medium 3 (25.05)

Usage: llm -m github/mistral-medium-2505

Publisher: Mistral AI

Description: Mistral Medium 3 is an advanced Large Language Model (LLM) with state-of-the-art reasoning, knowledge, coding and vision capabilities.

Mistral Small 3.1

Usage: llm -m github/mistral-small-2503

Publisher: Mistral AI

Description: Enhanced Mistral Small 3 with multimodal capabilities and a 128k context length.

OpenAI o1

Usage: llm -m github/o1

Publisher: OpenAI

Description: Focused on advanced reasoning and solving complex problems, including math and science tasks. Ideal for applications that require deep contextual understanding and agentic workflows.

OpenAI o1-mini

Usage: llm -m github/o1-mini

Publisher: OpenAI

Description: Smaller, faster, and 80% cheaper than o1-preview, performs well at code generation and small context operations.

OpenAI o1-preview

Usage: llm -m github/o1-preview

Publisher: OpenAI

Description: Focused on advanced reasoning and solving complex problems, including math and science tasks. Ideal for applications that require deep contextual understanding and agentic workflows.

OpenAI o3

Usage: llm -m github/o3

Publisher: OpenAI

Description: o3 includes significant improvements on quality and safety while supporting the existing features of o1 and delivering comparable or better performance.

OpenAI o3-mini

Usage: llm -m github/o3-mini

Publisher: OpenAI

Description: o3-mini includes the o1 features with significant cost-efficiencies for scenarios requiring high performance.

OpenAI o4-mini

Usage: llm -m github/o4-mini

Publisher: OpenAI

Description: o4-mini includes significant improvements on quality and safety while supporting the existing features of o3-mini and delivering comparable or better performance.

OpenAI Text Embedding 3 (large)

Usage: llm -m github/text-embedding-3-large

Publisher: OpenAI

Description: Text-embedding-3 series models are the latest and most capable embedding model from OpenAI.

OpenAI Text Embedding 3 (small)

Usage: llm -m github/text-embedding-3-small

Publisher: OpenAI

Description: Text-embedding-3 series models are the latest and most capable embedding model from OpenAI.