Text Generation
MLX
Safetensors
llada2_moe
dllm
diffusion
llm
text_generation
conversational
custom_code
4-bit precision
Instructions to use mlx-community/LLaDA2.0-mini-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/LLaDA2.0-mini-4bit 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("mlx-community/LLaDA2.0-mini-4bit") 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 mlx-community/LLaDA2.0-mini-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/LLaDA2.0-mini-4bit"
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": "mlx-community/LLaDA2.0-mini-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/LLaDA2.0-mini-4bit 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 "mlx-community/LLaDA2.0-mini-4bit"
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 mlx-community/LLaDA2.0-mini-4bit
Run Hermes
hermes
- OpenClaw new
How to use mlx-community/LLaDA2.0-mini-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/LLaDA2.0-mini-4bit"
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 "mlx-community/LLaDA2.0-mini-4bit" \ --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 mlx-community/LLaDA2.0-mini-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/LLaDA2.0-mini-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/LLaDA2.0-mini-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/LLaDA2.0-mini-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
| "architectures": [ | |
| "LLaDA2MoeModelLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_llada2_moe.LLaDA2MoeConfig", | |
| "AutoModel": "modeling_llada2_moe.LLaDA2MoeModel", | |
| "AutoModelForCausalLM": "modeling_llada2_moe.LLaDA2MoeModelLM" | |
| }, | |
| "dtype": "bfloat16", | |
| "embedding_dropout": 0.0, | |
| "first_k_dense_replace": 1, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5120, | |
| "max_position_embeddings": 32768, | |
| "max_window_layers": 28, | |
| "model_type": "llada2_moe", | |
| "moe_intermediate_size": 512, | |
| "moe_router_enable_expert_bias": true, | |
| "n_group": 8, | |
| "norm_head": false, | |
| "norm_softmax": false, | |
| "norm_topk_prob": true, | |
| "num_attention_heads": 16, | |
| "num_experts": 256, | |
| "num_experts_per_tok": 8, | |
| "num_hidden_layers": 20, | |
| "num_key_value_heads": 4, | |
| "num_shared_experts": 1, | |
| "output_dropout": 0.0, | |
| "output_router_logits": false, | |
| "pad_token_id": 156892, | |
| "partial_rotary_factor": 0.5, | |
| "quantization": { | |
| "group_size": 64, | |
| "bits": 4, | |
| "mode": "affine" | |
| }, | |
| "quantization_config": { | |
| "group_size": 64, | |
| "bits": 4, | |
| "mode": "affine" | |
| }, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 600000, | |
| "rotary_dim": 64, | |
| "routed_scaling_factor": 2.5, | |
| "router_dtype": "fp32", | |
| "score_function": "sigmoid", | |
| "sliding_window": 4096, | |
| "tie_word_embeddings": false, | |
| "topk_group": 4, | |
| "transformers_version": "4.57.1", | |
| "use_bias": false, | |
| "use_cache": false, | |
| "use_qkv_bias": false, | |
| "use_rmsnorm": true, | |
| "use_sliding_window": false, | |
| "using_split_qkv_in_self_attention": false, | |
| "vocab_size": 157184 | |
| } |