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gperdrizet/llms-mac

By gperdrizet

•Updated about 1 month ago

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gperdrizet/llms-mac repository overview

⁠LLM development Mac (ARM64) environment

A ready-to-use LLM application development environment for Apple Silicon Macs (M1/M2/M3). Includes LangChain, LlamaIndex, Hugging Face Transformers, and API clients for OpenAI and Anthropic. Built as a native linux/arm64 image — runs in Docker Desktop without Rosetta emulation.

Note: GPU acceleration is not available inside Docker containers on Apple Silicon: Metal/MPS is a macOS-only framework with no Docker passthrough. This image provides native ARM64 CPU performance only.

Note: This container is designed to be used as part of a VS Code Dev Container environment, not run directly. See the full environment setup on GitHub⁠ for devcontainer configuration and usage instructions.

⁠1. Features

  • Multi-framework support: LangChain, LlamaIndex, and Hugging Face Transformers pre-installed
  • API clients: OpenAI and Anthropic SDKs with LangChain integrations
  • Vector store: ChromaDB for embeddings and retrieval
  • Web UI: Gradio for building interactive demos
  • Native ARM64: No Rosetta emulation — runs at native speed on Apple Silicon
  • VS Code Dev Container ready: Pre-configured vscode user for seamless devcontainer integration

⁠2. Included software

ComponentVersion
Base Imagepython:3.12-slim
Platformlinux/arm64
PyTorchLatest (CPU, ARM64)
Python3.12
⁠2.1. LLM frameworks
  • langchain - LLM application framework
  • llama-index - Data framework for LLM applications
  • transformers - Hugging Face model hub and inference
  • smolagents - Hugging Face agents framework
⁠2.2. API clients
  • openai - OpenAI API client
  • anthropic - Anthropic Claude API client
  • ollama - Ollama server and Python client for local models
⁠2.3. Vector store and embeddings
  • chromadb - Embedded vector database
  • sentence-transformers - Text embeddings
⁠2.4. Additional tools
  • gradio - Web UI framework
  • accelerate - Model loading and distributed training
  • datasets - Hugging Face datasets
  • tiktoken - Token counting

⁠3. Usage with VS Code Dev Containers

This image is optimized for use with VS Code Dev Containers. The vscode user (UID 1000) is pre-configured with sudo access for a seamless development experience.

⁠4. Running directly with Docker

docker run --rm -it \
  --platform linux/arm64 \
  -v $(pwd):/workspace \
  gperdrizet/llms-mac:latest bash

Or launch a Gradio app:

docker run --platform linux/arm64 -p 7860:7860 -v $(pwd):/workspace gperdrizet/llms-mac:latest \
    python your_gradio_app.py

⁠5. Requirements

  • Docker Desktop for Mac (Apple Silicon)

⁠6. License

See the GitHub repository⁠ for license information.

Tag summary

Content type

Image

Digest

sha256:34ad9a7eb…

Size

5 GB

Last updated

about 1 month ago

docker pull gperdrizet/llms-mac