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Adapter model support

An adapter model is a model with X-LoRA or LoRA. X-LoRA support is provided by selecting an XLora* architecture, and LoRA support by selecting the Lora* architecture. For both X-LoRA and LoRA, an ordering file (see this section for preparing the ordering file) must be provided. The ordering file describes the ordering of layers and which adapters to use (and what order to use them in for X-LoRA).

When using an adapter model with a quantized base model, if the ordering file specifies unsupported layers you will receive an error.

Supported X-LoRA or LoRA quantized layers**

Llama architecture:

  • model.layers.{layer_idx}.self_attn.q_proj
  • model.layers.{layer_idx}.self_attn.k_proj
  • model.layers.{layer_idx}.self_attn.v_proj
  • model.layers.{layer_idx}.self_attn.o_proj
  • model.layers.{layer_idx}.mlp.up_proj
  • model.layers.{layer_idx}.mlp.down_proj
  • model.layers.{layer_idx}.mlp.gate_proj
  • lm_head

Phi 3 architecture:

  • model.layers.{layer_idx}.self_attn.qkv_proj
  • model.layers.{layer_idx}.self_attn.o_proj
  • model.layers.{layer_idx}.mlp.gate_up_proj
  • model.layers.{layer_idx}.mlp.down_proj
  • lm_head

Adapter ordering file

Preparing the X-LoRA/LoRA Ordering File The X-LoRA/LoRA ordering file is necessary to prepare before inference with an X-LoRA model. However, it is easy with a provided script!

X-LoRA case

An ordering JSON file for X-LoRA contains 2 major parts.

  1. The adapter names order
    • The order matters!
    • Should be an array of strings which are the adapter names corresponding to the order the adapters were specified during training. For example, if the adapters were specified as a dictionary:
  2. The layer ordering layers
    • Automatically generated and should not be manipulated as it controls the application of scalings.
adapters = {
    "math": ...,
    "reasoning": ...,
    "biology": ...
}

The specified order would be ["math", "reasoning", "biology"].

We provide an ordering file which contains the ordering for the X-LoRA model associated with the paper and the Huggingface repository: https://huggingface.co/lamm-mit/x-lora.

LoRA case

An ordering JSON file for LoRA contains 2 major parts:

  1. The adapter names order (optional):
    • The order does not matter
    • Come controls which adapters will be initially activated
    • If this key is not specified, then no adapters will be activated initially
  2. Preload adapter section preload_adapters (optional): see this section
    • Order does not matter
    • Specifies the adapter name and the model ID to find them, which may be a local path.

Preparing the ordering file (LoRA or X-LoRA cases)

There are 2 scripts to prepare the ordering file and which work for both X-LoRA and LoRA. The ordering file is specific to each architecture and set of target modules. Therefore, if either are changed, it is necessary to create a new ordering file using the first option. If only the adapter order or adapters changed, then it the second option should be used.

  1. From scratch: No ordering file for the architecture and target modules

    A script create_ordering.py is provided which prompts the user for the model ID, target modules, and adapter names. The user is prompted for an output file location, relative to the working directory.

  2. Create a new ordering file from an existing ordering file for an architecture and target modules

    A script set_names.py is provided which prompts the user for the adapter names and the old ordering file. The user is prompted for an output file location, relative to the working directory.

Quantized X-LoRA or LoRA models

Mistral.rs supports running quantized models with X-LoRA or LoRA. The X-LoRA or LoRA adapter layers will not be quantized, only the base model. P

In the X-LoRA case, please note that using a high quantization level (eg., 4-bit) can distort the signal and prevent the classifier from acting properly. Therefore, it is better to use slightly lower levels such as 8-bit.

Avoiding the scaling pass with non-granular scalings

The X-LoRA implementation supports non-granular scalings. This caches the scalings after k completion tokens are generated and they will be used for the remaining passes avoiding the scaling pass. The number of tokens to generate before caching is defined by setting tgt_non_granular_index. Setting tgt_non_granular_index will restrict the maximum running sequences to 1.

Please see this page for more details and examples.

Adapter model dynamic adapter activation

We support dynamic adapter activation for LoRA models, allowing you to activate a set of adapters at runtime. There is a Python, Rust and HTTP API:

To use this feature, you should add a preload_adapters key to your ordering file:

{
    "order": ["..."],
    "layers": {"...": "123"},
    "base_model_id": "...",
+    "preload_adapters": [{"name": "...", "adapter_model_id": "..."}] # New field here
}

This allows mistral.rs to preload the adapter and enable runtime activation.

We also provide a script to add this key to your existing order file: load_add_preload_adapters.py.