Text Generation
MLX
Safetensors
English
maple
mixture-of-experts
quantized
experimental
openmed
conversational
custom_code
4-bit precision
Instructions to use OpenMed/maple-preview-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
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
- Pi
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
File size: 2,722 Bytes
ce765ac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | {
"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": [
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention"
],
"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
} |