Instructions to use OpenMed/maple-preview-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenMed/maple-preview-4bit-mlx 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("OpenMed/maple-preview-4bit-mlx")

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

How to use OpenMed/maple-preview-4bit-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OpenMed/maple-preview-4bit-mlx"

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": "OpenMed/maple-preview-4bit-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use OpenMed/maple-preview-4bit-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OpenMed/maple-preview-4bit-mlx"

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 "OpenMed/maple-preview-4bit-mlx" \
  --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 OpenMed/maple-preview-4bit-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "OpenMed/maple-preview-4bit-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "OpenMed/maple-preview-4bit-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "OpenMed/maple-preview-4bit-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use OpenMed/maple-preview-4bit-mlx 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 "OpenMed/maple-preview-4bit-mlx"

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 OpenMed/maple-preview-4bit-mlx

Run Hermes

hermes

maple-preview-4bit-mlx / config.json

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	{
	"architectures": [
	"MapleForCausalLM"
	],
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_maple.MapleConfig",
	"AutoModel": "modeling_maple.MapleModel",
	"AutoModelForCausalLM": "modeling_maple.MapleForCausalLM"
	},
	"bos_token_id": 151643,
	"dtype": "bfloat16",
	"embedding_dropout": 0.0,
	"eos_token_id": 151645,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 2048,
	"initializer_range": 0.02,
	"intermediate_size": 4096,
	"layer_types": [
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	],
	"max_position_embeddings": 131072,
	"max_window_layers": 24,
	"model_file": "maple.py",
	"model_type": "maple",
	"moe_intermediate_size": 512,
	"moe_router_enable_expert_bias": false,
	"nope_on_global_attention": true,
	"norm_topk_prob": true,
	"num_attention_heads": 16,
	"num_experts": 256,
	"num_experts_per_tok": 8,
	"num_hidden_layers": 24,
	"num_key_value_heads": 4,
	"num_shared_experts": 0,
	"output_dropout": 0.0,
	"output_router_logits": false,
	"pad_token_id": null,
	"partial_rotary_factor": 0.5,
	"preaffine": false,
	"quantization": {
	"group_size": 128,
	"bits": 4,
	"mode": "affine",
	"model.word_embeddings": {
	"group_size": 64,
	"bits": 4
	},
	"lm_head": {
	"group_size": 64,
	"bits": 4
	}
	},
	"quantization_config": {
	"group_size": 128,
	"bits": 4,
	"mode": "affine",
	"model.word_embeddings": {
	"group_size": 64,
	"bits": 4
	},
	"lm_head": {
	"group_size": 64,
	"bits": 4
	}
	},
	"quantize": true,
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 10000,
	"router_dtype": "fp32",
	"sliding_window": 512,
	"tie_word_embeddings": false,
	"transformers_version": "4.57.1",
	"use_cache": true,
	"use_qk_norm": true,
	"use_rmsnorm": true,
	"vocab_size": 151936
	}