Text Generation
MLX
Safetensors
English
zaya
applescript
macos
computer-use
agent
tool-calling
function-calling
Mixture of Experts
osaurus
conversational
Instructions to use OsaurusAI/Osaurus-AppleScript-8B-JANG_6M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/Osaurus-AppleScript-8B-JANG_6M with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("OsaurusAI/Osaurus-AppleScript-8B-JANG_6M") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use OsaurusAI/Osaurus-AppleScript-8B-JANG_6M with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OsaurusAI/Osaurus-AppleScript-8B-JANG_6M"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "OsaurusAI/Osaurus-AppleScript-8B-JANG_6M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use OsaurusAI/Osaurus-AppleScript-8B-JANG_6M 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 "OsaurusAI/Osaurus-AppleScript-8B-JANG_6M"
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 OsaurusAI/Osaurus-AppleScript-8B-JANG_6M
Run Hermes
hermes
- OpenClaw new
How to use OsaurusAI/Osaurus-AppleScript-8B-JANG_6M with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OsaurusAI/Osaurus-AppleScript-8B-JANG_6M"
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 "OsaurusAI/Osaurus-AppleScript-8B-JANG_6M" \ --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"
- MLX LM
How to use OsaurusAI/Osaurus-AppleScript-8B-JANG_6M with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "OsaurusAI/Osaurus-AppleScript-8B-JANG_6M"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OsaurusAI/Osaurus-AppleScript-8B-JANG_6M" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OsaurusAI/Osaurus-AppleScript-8B-JANG_6M", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 1,643 Bytes
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"activation_func": "swiglu",
"activation_func_fp8_input_store": false,
"add_bias_linear": false,
"architectures": [
"ZayaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bias_activation_fusion": true,
"bos_token_id": 2,
"cca": true,
"dtype": "bfloat16",
"eos_token_id": 106,
"ffn_hidden_size": 4096,
"gated_linear_unit": true,
"hidden_size": 2048,
"head_dim": 128,
"kv_channels": 128,
"lm_head_bias": false,
"mamba_cache_dtype": "float32",
"max_position_embeddings": 131072,
"model_type": "zaya",
"moe_router_topk": 1,
"norm_epsilon": 1e-05,
"normalization": "RMSNorm",
"num_attention_heads": 8,
"num_experts": 16,
"num_hidden_layers": 80,
"num_key_value_heads": 2,
"num_query_groups": 2,
"pad_token_id": 0,
"partial_rotary_factor": 0.5,
"residual_in_fp32": true,
"rope_scaling": false,
"rope_theta": 5000000,
"scale_residual_merge": true,
"sliding_window": null,
"transformers_version": "4.57.1",
"use_cache": true,
"vocab_size": 262272,
"zaya_mlp_expansion": 256,
"zaya_use_eda": true,
"zaya_use_mod": true,
"weight_format": "mxfp4",
"zaya_expert_layout": "split_switch_mlp",
"tie_word_embeddings": true,
"quantization": {
"bits": 6,
"group_size": 32,
"mode": "affine",
"embed_bits": 8,
"router_bits": 16,
"expert_layout": "split_switch_mlp"
},
"capabilities": {
"reasoning_parser": "qwen3",
"tool_parser": "zaya_xml",
"think_in_template": false,
"supports_tools": true,
"supports_thinking": true,
"family": "zaya",
"modality": "text",
"cache_type": "hybrid"
}
} |