Image-Text-to-Text
MLX
Safetensors
inkling_mm_model
Mixture of Experts
multimodal
inkling
thinking-machines
conversational
Instructions to use pipenetwork/Inkling-Small-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use pipenetwork/Inkling-Small-MLX-4bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("pipenetwork/Inkling-Small-MLX-4bit") config = load_config("pipenetwork/Inkling-Small-MLX-4bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use pipenetwork/Inkling-Small-MLX-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 "pipenetwork/Inkling-Small-MLX-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": "pipenetwork/Inkling-Small-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use pipenetwork/Inkling-Small-MLX-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 "pipenetwork/Inkling-Small-MLX-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 pipenetwork/Inkling-Small-MLX-4bit
Run Hermes
hermes
- OpenClaw new
How to use pipenetwork/Inkling-Small-MLX-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 "pipenetwork/Inkling-Small-MLX-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 "pipenetwork/Inkling-Small-MLX-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"
File size: 2,299 Bytes
cc1f871 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | {
"architectures": [
"InklingForConditionalGeneration"
],
"model_type": "inkling_mm_model",
"eos_token_id": 200006,
"text_config": {
"model_max_length": 1048576,
"torch_dtype": "bfloat16",
"hidden_size": 4096,
"num_hidden_layers": 42,
"vocab_size": 201024,
"num_attention_heads": 32,
"num_key_value_heads": 8,
"head_dim": 128,
"d_rel": 16,
"rel_extent": 1024,
"q_bias": false,
"o_bias": false,
"log_scaling_n_floor": 128000,
"log_scaling_alpha": 0.1,
"rms_norm_eps": 1e-06,
"use_embed_norm": true,
"local_layer_ids": [
0,
1,
2,
3,
4,
6,
7,
8,
9,
10,
12,
13,
14,
15,
16,
18,
19,
20,
21,
22,
24,
25,
26,
27,
28,
30,
31,
32,
33,
34,
36,
37,
38,
39,
40
],
"dense_mlp_idx": 2,
"use_sconv": true,
"sconv_kernel_size": 4,
"unpadded_vocab_size": 200058,
"logits_mup_width_multiplier": 16.0,
"final_logit_softcapping": null,
"swa_head_dim": 128,
"swa_num_attention_heads": 32,
"swa_num_key_value_heads": 8,
"sliding_window_size": 512,
"n_routed_experts": 256,
"num_experts_per_tok": 6,
"n_shared_experts": 2,
"shared_expert_sink": true,
"dense_intermediate_size": 16384,
"intermediate_size": 2048,
"route_scale": 8.0,
"use_gate_bias": true,
"gate_activation": "sigmoid",
"norm_after_topk": true,
"use_global_scale": true
},
"audio_config": {
"decoder_dmodel": 4096,
"n_mel_bins": 80,
"mel_vocab_size": 16,
"bias": false,
"dmel_min_value": -7.0,
"dmel_max_value": 2.0,
"use_audio_norm": true,
"audio_mode": "dmel"
},
"vision_config": {
"vision_encoder_type": "hmlp",
"decoder_dmodel": 4096,
"patch_size": 40,
"temporal_patch_size": 2,
"n_channels": 3,
"n_layers": 4,
"use_vision_norm": true
},
"mtp_config": {
"num_nextn_predict_layers": 8,
"chain_hidden_post_norm": false,
"local_layer_ids": [
0,
2,
4,
5,
6,
7
]
},
"quantization": {
"group_size": 64,
"bits": 4,
"recipe": "uniform"
}
} |