> ## Documentation Index
> Fetch the complete documentation index at: https://internal.nolano.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# ModelConfig

> API reference for model configuration

## ModelConfig

<ParamField path="architecture" type="str" required>
  Model architecture specification. Supports major dense and MoE Hugging Face architectures including Qwen, LLaMA, Gemma.
</ParamField>

<ParamField path="init_method" type="str" default="normal">
  Weight initialization strategy:

  * `"none"`: Load from pre-trained model (Qwen/LLaMA/Gemma)
  * `"normal"`: Normal distribution initialization
  * `"xavier_uniform"`: Xavier uniform initialization
  * `"wang_init"`: Wang initialization method
</ParamField>

<ParamField path="model_path" type="str | None" default="None">
  Path to pre-trained model for continual training. Must be `None` if `init_method` is not `"none"`.
</ParamField>

<ParamField path="load_optimizer" type="bool | None" default="None">
  Whether to load optimizer state from checkpoint. Set to `True` for continual training from checkpoint.
</ParamField>

<ParamField path="precision" type="str" default="fp16">
  Model precision configuration:

  * `"binary"`: Binary precision (1-bit)
  * `"ternary"`: Ternary precision (1.58-bit)
  * `"int2"`: 2-bit integer precision
  * `"fp8"`: 8-bit floating point
  * `"mxfp4"`: 4-bit microscaling floating point
  * `"mxfp6"`: 6-bit microscaling floating point
  * `"ue8m0"`: 8-bit unsigned integer with 0 exponent bits
  * `"fp16"`: 16-bit floating point (default)
  * `"fp32"`: 32-bit floating point
</ParamField>