Text Generation
MLX
Safetensors
bailing_hybrid
Mixture of Experts
mixture-of-experts
hybrid-attention
mla
lightning-attention
mxfp4
osaurus
bailing
ling
apple-silicon
conversational
custom_code
Instructions to use OsaurusAI/Ling-2.6-flash-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/Ling-2.6-flash-MXFP4 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/Ling-2.6-flash-MXFP4") 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/Ling-2.6-flash-MXFP4 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/Ling-2.6-flash-MXFP4"
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": "OsaurusAI/Ling-2.6-flash-MXFP4" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use OsaurusAI/Ling-2.6-flash-MXFP4 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/Ling-2.6-flash-MXFP4"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OsaurusAI/Ling-2.6-flash-MXFP4" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OsaurusAI/Ling-2.6-flash-MXFP4", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use OsaurusAI/Ling-2.6-flash-MXFP4 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/Ling-2.6-flash-MXFP4"
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/Ling-2.6-flash-MXFP4
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use OsaurusAI/Ling-2.6-flash-MXFP4 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/Ling-2.6-flash-MXFP4"
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/Ling-2.6-flash-MXFP4" \ --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"
Upload jang_config.json with huggingface_hub
Browse files- jang_config.json +25 -0
jang_config.json
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{
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"version": 2,
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"weight_format": "mxfp4",
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"profile": "MXFP4",
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"source_model": {
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"name": "Ling-2.6-flash",
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"org": "inclusionAI",
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"architecture": "bailing_hybrid"
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},
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"quantization": {
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"method": "affine",
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"group_size": 32,
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"bits": 4
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},
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"capabilities": {
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"reasoning_parser": "deepseek_r1",
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"tool_parser": "deepseek",
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"think_in_template": false,
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"supports_tools": true,
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"supports_thinking": true,
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"family": "bailing_hybrid",
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"modality": "text",
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"cache_type": "hybrid"
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}
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}
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