Repository: ggerganov/llama.cpp
File: convert_hf_to_gguf.py (Python)

Code before:

        # ICLR: In-Context-Learning-Rate
        lora_rank_decay = 64
        lora_rank_iclr = 64
        lora_rank_value_residual_mix = 32
        lora_rank_gate = 128 if wkv_has_gate else 0

        # RWKV isn't context limited
        self.gguf_writer.add_context_length(1048576)
        self.gguf_writer.add_embedding_length(hidden_size)
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
        self.gguf_writer.add_wkv_head_size(head_size)
        self.gguf_writer.add_decay_lora_rank(lora_rank_decay)
        self.gguf_writer.add_iclr_lora_rank(lora_rank_iclr)
        self.gguf_writer.add_value_residual_mix_lora_rank(lora_rank_value_residual_mix)
        self.gguf_writer.add_gate_lora_rank(lora_rank_gate)
        self.gguf_writer.add_feed_forward_length(intermediate_size)
        self.gguf_writer.add_file_type(self.ftype)
        self.gguf_writer.add_token_shift_count(1)

        # required by llama.cpp, unused
        self.gguf_writer.add_head_count(0)

@ModelBase.register("MaincoderForCausalLM")
class MaincoderModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MAINCODER

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        if (head_dim := self.hparams.get("head_dim")) is not None:
            self.gguf_writer.add_rope_dimension_count(head_dim)


@ModelBase.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
class MambaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MAMBA

    def __init__(self, dir_model: Path, *args, **kwargs):
        # Avoid using AutoConfig for hparams
        hparams = kwargs.pop("hparams", None)
        if hparams is None:
            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
                hparams = json.load(f)
        super().__init__(dir_model, *args, hparams=hparams, **kwargs)

    def set_vocab(self):
        vocab_size = self.hparams["vocab_size"]
        # Round vocab size to next multiple of 8
        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)

Review comment (suggestion):
```suggestion

@ModelBase.register("MaincoderForCausalLM")
```
One more. :)

Code after:

        # ICLR: In-Context-Learning-Rate
        lora_rank_decay = 64
        lora_rank_iclr = 64
        lora_rank_value_residual_mix = 32
        lora_rank_gate = 128 if wkv_has_gate else 0

        # RWKV isn't context limited
        self.gguf_writer.add_context_length(1048576)
        self.gguf_writer.add_embedding_length(hidden_size)
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
        self.gguf_writer.add_wkv_head_size(head_size)
        self.gguf_writer.add_decay_lora_rank(lora_rank_decay)
        self.gguf_writer.add_iclr_lora_rank(lora_rank_iclr)
        self.gguf_writer.add_value_residual_mix_lora_rank(lora_rank_value_residual_mix)
        self.gguf_writer.add_gate_lora_rank(lora_rank_gate)
        self.gguf_writer.add_feed_forward_length(intermediate_size)
        self.gguf_writer.add_file_type(self.ftype)
        self.gguf_writer.add_token_shift_count(1)

        # required by llama.cpp, unused
        self.gguf_writer.add_head_count(0)


@ModelBase.register("MaincoderForCausalLM")
class MaincoderModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MAINCODER

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        if (head_dim := self.hparams.get("head_dim")) is not None:
            self.gguf_writer.add_rope_dimension_count(head_dim)


@ModelBase.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
class MambaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MAMBA

    def __init__(self, dir_model: Path, *args, **kwargs):
        # Avoid using AutoConfig for hparams
        hparams = kwargs.pop("hparams", None)
        if hparams is None:
            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
                hparams = json.load(f)
        super().__init__(dir_model, *args, hparams=hparams, **kwargs)

    def set_vocab(self):
        vocab_size = self.hparams["vocab_size"]
        # Round vocab size to next multiple of 8