Instructions to use studiocatalyst/atlas-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use studiocatalyst/atlas-models with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf studiocatalyst/atlas-models:Q5_K_M --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf studiocatalyst/atlas-models:Q5_K_M --embeddings
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible embeddings server: ./build/bin/llama serve -hf studiocatalyst/atlas-models:Q5_K_M --embeddings
- Llama new
- LM Studio
- Jan
- Ollama
How to use studiocatalyst/atlas-models with Ollama:
ollama run hf.co/studiocatalyst/atlas-models:Q5_K_M
- Unsloth Desktop
- Docker Model Runner
How to use studiocatalyst/atlas-models with Docker Model Runner:
docker model run hf.co/studiocatalyst/atlas-models:Q5_K_M
- Lemonade
How to use studiocatalyst/atlas-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull studiocatalyst/atlas-models:Q5_K_M
Run and chat with the model
lemonade run user.atlas-models-Q5_K_M
List all available models
lemonade list
- Atomic Chat
Catalyst Atlas models
The model files Catalyst Atlas downloads on first use, each pinned by its SHA-256. They are other authors' models, converted or quantized to run locally; none was trained or fine-tuned here. Each keeps its own licence, below.
| File | Model | What was done | Licence | SHA-256 |
|---|---|---|---|---|
embeddinggemma-2-UD-Q5_K_XL.gguf |
EmbeddingGemma 2 by Google DeepMind: its text model | None: an unchanged copy of Unsloth's dynamic Q5_K_XL quantization | Apache 2.0 | a9d7a3b7eb421113f05e964e10882676f10fff83811b7876d5bb983db7c7ac41 |
mmproj-Q5_1.gguf |
EmbeddingGemma 2 by Google DeepMind: its picture and sound encoders with their projectors | Quantized to Q5_1 (the linear layers only) from ggml-org's BF16 GGUF | Apache 2.0 | cc85c7137a34989ef5fb6fee08725671c936276cff5eb820b93ff02c39528906 |
omnishotcut_v1.5_fp16.onnx |
OmniShotCut v1.5 by the UVA Computer Vision Lab: finds the cuts between shots | Converted to ONNX, in 16-bit floats | MIT | 9c39c117eafe6cd6939c6236ccb274076c7c9c4a2927de3ac606246694ac05d3 |
Qwen3-ASR-1.7B.i1-Q5_K_M.gguf |
Qwen3-ASR 1.7B by the Qwen team, Alibaba Cloud: speech recognition | None: an unchanged copy of mradermacher's Q5_K_M with an importance matrix | Apache 2.0 | 737f4b9cb049361dad7d4625d298b16a0a2cb026268da195b93298329636a304 |
Qwen3-ASR-1.7B.mmproj-Q8_0.gguf |
Qwen3-ASR 1.7B: its audio encoder with its projector | None: an unchanged copy of mradermacher's Q8_0 | Apache 2.0 | 780b5b48dfbc23c219cc9d3da9fdaccc7da567fa661b3724d19bda50fbcfc8c3 |
Licences and notices
- Apache 2.0 (
LICENSE-Apache-2.0.txt): EmbeddingGemma 2 and Qwen3-ASR. The files marked above as quantized or converted were modified from the originals in that way only. - MIT (
LICENSE-MIT-OmniShotCut.txt): OmniShotCut, Copyright (c) 2026 UVA Computer Vision Lab. - "Gemma" and "EmbeddingGemma" are trademarks of Google LLC. EmbeddingGemma 2's model card asks that its use follow Google's Gemma Prohibited Use Policy.
- The models are provided as their authors provide them, without warranties of any kind.
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