Image-Text-to-Text
MLX
Safetensors
GGUF
English
mistral3
aviation
chatbot
Q+A
flying
airplane
aviation engineering
airframe
atlas-2
atlas-2-small
text-generation
conversational
aeroai
aeroaiaviation
Q_4_KM
atlas-2.1
multimodal
4-bit precision
Instructions to use AeroAIAviation/Atlas-2.1-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AeroAIAviation/Atlas-2.1-Small with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("AeroAIAviation/Atlas-2.1-Small") config = load_config("AeroAIAviation/Atlas-2.1-Small") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AeroAIAviation/Atlas-2.1-Small with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf AeroAIAviation/Atlas-2.1-Small:Q4_K_M # Run inference directly in the terminal: llama cli -hf AeroAIAviation/Atlas-2.1-Small:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AeroAIAviation/Atlas-2.1-Small:Q4_K_M # Run inference directly in the terminal: llama cli -hf AeroAIAviation/Atlas-2.1-Small:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf AeroAIAviation/Atlas-2.1-Small:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AeroAIAviation/Atlas-2.1-Small:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf AeroAIAviation/Atlas-2.1-Small:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AeroAIAviation/Atlas-2.1-Small:Q4_K_M
Use Docker
docker model run hf.co/AeroAIAviation/Atlas-2.1-Small:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use AeroAIAviation/Atlas-2.1-Small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AeroAIAviation/Atlas-2.1-Small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AeroAIAviation/Atlas-2.1-Small", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/AeroAIAviation/Atlas-2.1-Small:Q4_K_M
- Ollama
How to use AeroAIAviation/Atlas-2.1-Small with Ollama:
ollama run hf.co/AeroAIAviation/Atlas-2.1-Small:Q4_K_M
- Unsloth Desktop
- Pi
How to use AeroAIAviation/Atlas-2.1-Small with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AeroAIAviation/Atlas-2.1-Small"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "AeroAIAviation/Atlas-2.1-Small" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use AeroAIAviation/Atlas-2.1-Small with Docker Model Runner:
docker model run hf.co/AeroAIAviation/Atlas-2.1-Small:Q4_K_M
- Lemonade
How to use AeroAIAviation/Atlas-2.1-Small with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AeroAIAviation/Atlas-2.1-Small:Q4_K_M
Run and chat with the model
lemonade run user.Atlas-2.1-Small-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use AeroAIAviation/Atlas-2.1-Small with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AeroAIAviation/Atlas-2.1-Small"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default AeroAIAviation/Atlas-2.1-Small
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use AeroAIAviation/Atlas-2.1-Small with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "AeroAIAviation/Atlas-2.1-Small"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "AeroAIAviation/Atlas-2.1-Small" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download params.json from AeroAIAviation/Atlas-2.1-Small: direct link, hf CLI and curl.
- Browser
- Download file 1.18 kB
-
https://huggingface.co/AeroAIAviation/Atlas-2.1-Small/resolve/main/params.json
- Command line
-
hf download hf://AeroAIAviation/Atlas-2.1-Small/params.json
-
curl -L -o params.json https://huggingface.co/AeroAIAviation/Atlas-2.1-Small/resolve/main/params.json
1.18 kB
| { | |
| "dim": 3072, | |
| "n_layers": 26, | |
| "head_dim": 128, | |
| "hidden_dim": 9216, | |
| "n_heads": 32, | |
| "n_kv_heads": 8, | |
| "rope_theta": 1000000.0, | |
| "norm_eps": 1e-05, | |
| "vocab_size": 131072, | |
| "tied_embeddings": true, | |
| "max_position_embeddings": 262144, | |
| "llama_4_scaling": { | |
| "original_max_position_embeddings": 16384, | |
| "beta": 0.1 | |
| }, | |
| "q_lora_rank": null, | |
| "qk_rope_head_dim": null, | |
| "qk_nope_head_dim": null, | |
| "kv_lora_rank": null, | |
| "v_head_dim": null, | |
| "quantization": { | |
| "qformat_weight": "fp8_e4m3", | |
| "qscheme_act": "TENSOR" | |
| }, | |
| "yarn": { | |
| "original_max_position_embeddings": 16384, | |
| "factor": 16, | |
| "apply_scale": false, | |
| "beta": 32, | |
| "alpha": 1 | |
| }, | |
| "vision_encoder": { | |
| "image_token_id": 10, | |
| "intermediate_size": 4096, | |
| "num_hidden_layers": 24, | |
| "num_attention_heads": 16, | |
| "mm_projector_id": "patch_merge", | |
| "spatial_merge_size": 2, | |
| "hidden_size": 1024, | |
| "num_channels": 3, | |
| "image_size": 1540, | |
| "max_image_size": 1540, | |
| "patch_size": 14, | |
| "rope_theta": 10000.0, | |
| "add_pre_mm_projector_layer_norm": true, | |
| "adapter_bias": false, | |
| "image_break_token_id": 12, | |
| "image_end_token_id": 13 | |
| } | |
| } |