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Signed-off-by: Daniel J Walsh <dwalsh@redhat.com>

Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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Daniel J Walsh
2026-01-06 10:19:20 -05:00
committed by GitHub
parent 059945f3d8
commit b9f671646e
1 changed files with 4 additions and 4 deletions
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CLAUDE.md
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@@ -4,7 +4,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
## Project Overview
RamaLama is a CLI tool for managing and serving AI models using containers. It provides a container-centric approach to AI model management, supporting multiple model registries (HuggingFace, Ollama, OCI registries) and automatic GPU detection with appropriate container image selection.
RamaLama is a CLI tool for managing and serving AI models using containers. It provides a container-centric approach to AI model management, supporting multiple model registries (Hugging Face, Ollama, OCI registries) and automatic GPU detection with appropriate container image selection.
## Build and Development Commands
@@ -31,7 +31,7 @@ make bats-nocontainer # Run in nocontainer mode
make bats-docker # Run with Docker
# All tests
make tests # Run unit + end-to-end tests
make tests # Run unit tests and system-level integration tests
```
### Running a single test
@@ -48,7 +48,7 @@ RAMALAMA=$(pwd)/bin/ramalama bats -T test/system/030-run.bats
### Code Quality
```bash
make validate # Run all validation (codespell, lint, format check, type check)
make validate # Run all validation (codespell, lint, format check, man-check, type check)
make lint # Run flake8 + shellcheck
make check-format # Check black + isort formatting
make format # Auto-format with black + isort
@@ -88,7 +88,7 @@ Manages local model storage:
- `schema.py` - Inference spec schema handling
### Key Patterns
- **GPU Detection**: `get_accel()` in `common.py` detects GPU type (cuda, rocm, vulkan, etc.) and selects appropriate container image
- **GPU Detection**: `get_accel()` in `common.py` detects GPU type (CUDA, ROCm, Vulkan, etc.) and selects appropriate container image
- **Container Images**: GPU-specific images at `quay.io/ramalama/{ramalama,cuda,rocm,intel-gpu,...}`
- **Inference Engines**: llama.cpp (default), vllm, mlx (macOS only) - configured via YAML specs in `inference-spec/engines/`