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TdAI / llama.cpp /docs /development /HOWTO-add-model.md
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Add a new model architecture to llama.cpp

Adding a model requires few steps:

  1. Convert the model to GGUF
  2. Define the model architecture in llama.cpp
  3. Build the GGML graph implementation
  4. Optional: Add multimodal encoder implementation

After following these steps, you can open PR.

Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:

1. Convert the model to GGUF

This step is done in python with a convert script using the gguf library. Depending on the model architecture, you can use either convert_hf_to_gguf.py or examples/convert_legacy_llama.py (for llama/llama2 models in .pth format).

The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.

The required steps to implement for an HF model are:

  1. Define the model ModelBase.register annotation in a new TextModel or MmprojModel subclass in the conversion folder, example:
@ModelBase.register("MyModelForCausalLM")
class MyModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MYMODEL

or

@ModelBase.register("MyModelForConditionalGeneration")
class MyModel(MmprojModel):
    model_arch = gguf.MODEL_ARCH.MYMODEL
  1. Define the layout of the GGUF tensors in constants.py

Add an enum entry in MODEL_ARCH, the model human friendly name in MODEL_ARCH_NAMES and the GGUF tensor names in MODEL_TENSORS.

Example for falcon model:

    MODEL_ARCH.FALCON: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
        MODEL_TENSOR.OUTPUT,
        MODEL_TENSOR.ATTN_NORM,
        MODEL_TENSOR.ATTN_NORM_2,
        MODEL_TENSOR.ATTN_QKV,
        MODEL_TENSOR.ATTN_OUT,
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ]
  1. Map the original tensor names to the standardize equivalent in GGUF

As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist.

Once you have found the GGUF tensor name equivalent, add it to the tensor_mapping.py file.

If the tensor name is part of a repetitive layer/block, the key word bid substitutes it.

Example for the normalization tensor in attention layers:

block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
        # Attention norm
        MODEL_TENSOR.ATTN_NORM: (
            "gpt_neox.layers.{bid}.input_layernorm",                # gptneox
            "transformer.h.{bid}.ln_1",                             # gpt2 gpt-j refact qwen
            "transformer.blocks.{bid}.norm_1",                      # mpt
            ...
        )
}

transformer.blocks.{bid}.norm_1 will be mapped to blk.{bid}.attn_norm in GGUF.

Depending on the model configuration, tokenizer, code and tensors layout, you will have to override:

  • TextModel#set_gguf_parameters
  • MmprojModel#set_gguf_parameters
  • ModelBase#set_vocab
  • ModelBase#modify_tensors

NOTE: Tensor names must end with .weight or .bias suffixes, that is the convention and several tools like quantize expect this to proceed the weights.

2. Define the model architecture in llama.cpp

The model params and tensors layout must be defined in llama.cpp source files:

  1. Define a new llm_arch enum value in src/llama-arch.h.
  2. In src/llama-arch.cpp:
    • Add the architecture name to the LLM_ARCH_NAMES map.
    • You may also need to update LLM_KV_NAMES, LLM_TENSOR_NAMES and LLM_TENSOR_INFOS
  3. Add any non-standard metadata loading in the llama_model_loader constructor in src/llama-model-loader.cpp.
  4. If the model has a RoPE operation, add a case for the architecture in llama_model_rope_type function in src/llama-model.cpp.

NOTE: The dimensions in ggml are typically in the reverse order of the pytorch dimensions.

3. Build the GGML graph implementation

This is the funniest part, you have to provide the inference graph implementation of the new model architecture in src/llama-model.cpp:

  1. Create a new struct that inherits from llama_model_base.
  2. Implement the graph-building logic in its build_arch_graph method.
  3. The build_arch_graph method should return a constructed graph (inherited from llm_graph_context). Have a look at existing implementations like llama_model_llama, llama_model_dbrx or llama_model_bert.
  4. Then, in the llama_model_mapping function, add a case for your architecture to instantiate your new graph-building struct.

Some ggml backends do not support all operations. Backend implementations can be added in a separate PR.

Note: to debug the inference graph: you can use llama-eval-callback.

4. Optional: Add multimodal encoder implementation

If the new model supports multimodal inputs, you will need to add a new encoder definition in libmtmd. You can find more information about llama.cpp's multimodal support in the docs and in the tools/mtmd source directory.

  1. In the conversion script, make sure you add a subclass that extends MmprojModel or another class that inherits from the same base class.
  2. Add the encoder definition in clip.cpp.
  3. Implement the preprocessor in mtmd.cpp. In most cases, you can reuse an existing preprocessor.
  4. Implement the encoder GGML graph, either in a dedicated file if the model is truly different from existing ones, or by reusing an existing implementation (for example: siglip, pixtral, or qwen) and adding a model-specific projector.

Note:

  • Many multimodal encoders are based on models that are already supported. Make sure to read the existing encoder definitions in tools/mtmd/models before adding a new one. In libmtmd, it is generally better to extend an existing model than to duplicate code.
  • To debug the multimodal preprocessor and encoder, you can use llama-mtmd-debug.
  • Adding a model-specific API or CLI is an anti-pattern in libmtmd. The goal of libmtmd is to provide an easy-to-use, model-agnostic library for multimodal pipeline.
  • In most cases, llama-mtmd-cli should not be modified. If a model requires a specific prompt, either let the user provide it or bake it into the Jinja chat template.

Tips and tricks

Working with ggml_rope_ext

PyTorch implementations usually prefer explicitly calculating freq_cis/sin/cos components. However, in llama.cpp, most RoPE operations can be handled via ggml_rope_ext, which does not require a sin/cos matrix. This saves memory while allowing the GGML RoPE kernel to be fused with other ops.

However, since ggml_rope_ext only provides a subset of the RoPE implementations that models use, converting models from PyTorch to llama.cpp may require some creative adaptations.

For more information about ggml_rope_ext, please refer to the in-code documentation in ggml.h.

Examples:

  • libmtmd implements 2D RoPE with GGML_ROPE_TYPE_NORMAL ordering by splitting the input tensor in half, applying ggml_rope_ext separately to each half, then joining them back together using ggml_concat.
  • The Kimi-K2.5 vision encoder uses vision RoPE with interleaved frequencies. The weights must be permuted during conversion in order to reuse the build_rope_2d() function.
  • Gemma 4 uses "proportional" RoPE. We employ a trick where rope_freqs is set to a very large value in the last dimensions to prevent those dimensions from being rotated. See the Gemma4Model class in convert_hf_to_gguf.py.
  • Some models require scaling the input position. For example, [0, 1, 2, ...] becomes [0, 0.5, 1, ...]. In this case, you can provide the scaling via freq_scale = 0.5f.
  • Some models use learned RoPE frequencies instead of relying on powf(freq_base, -2.0 * i / n_dims). In this case, you can provide the learned frequencies via the rope_freqs tensor (corresponding to the c argument in ggml_rope_ext), then set freq_base = 1.0f. An important note is that rope_freqs in GGML is the inverse (theta = pos[i] / rope_freqs), so you may need to invert rope_freqs during conversion.

GGUF specification

https://github.com/ggml-org/ggml/blob/master/docs/gguf.md

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