Text Generation
MLX
Safetensors
English
Chinese
bailing_hybrid
jang
jangtq
jangtq2
turboquant
quantized
mixed-precision
apple-silicon
Mixture of Experts
bailing
bailing-hybrid
linear-attention
mla
multi-latent-attention
abliterated
uncensored
crack
harmbench
mmlu
bilingual
ling
ling-2.6
inclusionai
conversational
custom_code
Instructions to use dealignai/Ling-2.6-flash-JANGTQ2-CRACK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dealignai/Ling-2.6-flash-JANGTQ2-CRACK 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("dealignai/Ling-2.6-flash-JANGTQ2-CRACK") 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 dealignai/Ling-2.6-flash-JANGTQ2-CRACK with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "dealignai/Ling-2.6-flash-JANGTQ2-CRACK"
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": "dealignai/Ling-2.6-flash-JANGTQ2-CRACK" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use dealignai/Ling-2.6-flash-JANGTQ2-CRACK with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "dealignai/Ling-2.6-flash-JANGTQ2-CRACK"
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 "dealignai/Ling-2.6-flash-JANGTQ2-CRACK" \ --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 dealignai/Ling-2.6-flash-JANGTQ2-CRACK with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "dealignai/Ling-2.6-flash-JANGTQ2-CRACK"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "dealignai/Ling-2.6-flash-JANGTQ2-CRACK" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dealignai/Ling-2.6-flash-JANGTQ2-CRACK", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use dealignai/Ling-2.6-flash-JANGTQ2-CRACK 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 "dealignai/Ling-2.6-flash-JANGTQ2-CRACK"
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 dealignai/Ling-2.6-flash-JANGTQ2-CRACK
Run Hermes
hermes
| { | |
| "version": 2, | |
| "weight_format": "mxtq", | |
| "profile": "JANGTQ2", | |
| "source_model": { | |
| "name": "Ling-2.6-flash", | |
| "org": "inclusionAI", | |
| "architecture": "bailing_hybrid" | |
| }, | |
| "mxtq_seed": 42, | |
| "mxtq_bits": { | |
| "routed_expert": 2, | |
| "attention": 8, | |
| "shared_expert": 8, | |
| "dense_mlp": 8, | |
| "embed_tokens": 8, | |
| "lm_head": 8, | |
| "mtp_eh_proj": 8, | |
| "norms_router_biases": 16 | |
| }, | |
| "quantization": { | |
| "method": "affine+mxtq", | |
| "group_size": 64, | |
| "bits_default": 2 | |
| }, | |
| "capabilities": { | |
| "reasoning_parser": "deepseek_r1", | |
| "tool_parser": "deepseek", | |
| "think_in_template": false, | |
| "supports_tools": true, | |
| "supports_thinking": true, | |
| "family": "bailing_hybrid", | |
| "modality": "text", | |
| "cache_type": "hybrid" | |
| }, | |
| "routed_expert_layout": "prestacked" | |
| } |