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

Code before:
@ModelBase.register("JambaForCausalLM")
class JambaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.JAMBA

    def get_vocab_base_pre(self, tokenizer) -> str:
        del tokenizer  # unused

        return "default"

    def set_vocab(self):
        if (self.dir_model / "tokenizer.model").is_file():
            self._set_vocab_sentencepiece()
        else:
            tokens: list[str] = []
            toktypes: list[int] = []

            from transformers import AutoTokenizer

            tokenizer = AutoTokenizer.from_pretrained(
                self.dir_model, trust_remote_code=True
            )
            vocab = getattr(tokenizer, "vocab", tokenizer.get_vocab())
            vocab_size = self.hparams.get("vocab_size", len(vocab))
            assert max(vocab.values()) < vocab_size

            tokpre = self.get_vocab_base_pre(tokenizer)

            reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()}
            added_vocab = tokenizer.get_added_vocab()

            added_tokens_decoder = tokenizer.added_tokens_decoder

            for i in range(vocab_size):
                if i not in reverse_vocab:
                    tokens.append(f"[PAD{i}]")
                    toktypes.append(gguf.TokenType.UNUSED)
                else:
                    token: str = reverse_vocab[i]

                    if token in added_vocab:
                        if not added_tokens_decoder[i].normalized:
                            previous_token = token
                            token = tokenizer.decode(
                                tokenizer.encode(token, add_special_tokens=False)
                            )
                            if previous_token != token:
                                logger.info(
                                    f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer"
                                )

                        if added_tokens_decoder[i].special or self.does_token_look_special(

Review comment (suggestion):
```suggestion
            assert max(vocab.values()) < vocab_size
```

Code after:
@ModelBase.register("JambaForCausalLM")
class JambaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.JAMBA

    def set_vocab(self):
        if (self.dir_model / "tokenizer.model").is_file():
            self._set_vocab_sentencepiece()
        else:
            self._set_vocab_llama_hf()
            self.gguf_writer.add_add_space_prefix(False)

    def set_gguf_parameters(self):
        d_model = self.find_hparam(["hidden_size", "mamba_d_model"])
        d_conv  = self.find_hparam(["mamba_d_conv"],  optional=True) or 4
        d_inner = self.hparams["mamba_expand"] * d_model
        d_state = self.find_hparam(["mamba_d_state"], optional=True) or 16
        # ceiling division
        # ref: https://stackoverflow.com/a/17511341/22827863
        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
        dt_rank      = self.find_hparam(["mamba_dt_rank"], optional=True) or -(d_model // -16)
        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-6
        n_kv_head = self.hparams["num_key_value_heads"]
        attn_offset = self.hparams["attn_layer_offset"]
        attn_period = self.hparams["attn_layer_period"]
        n_kv_vec = [0 for _ in range(attn_offset)] + [
            n_kv_head if (i - attn_offset) % attn_period == 0 else 0 for i in range(attn_offset, self.block_count)
        ]

        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_context_length(self.find_hparam(["max_position_embeddings", "n_ctx"]))
        self.gguf_writer.add_embedding_length(d_model)
        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
        self.gguf_writer.add_head_count_kv(n_kv_vec)
        self.gguf_writer.add_ssm_conv_kernel(d_conv)
        self.gguf_writer.add_ssm_inner_size(d_inner)
        self.gguf_writer.add_ssm_state_size(d_state)
        self.gguf_writer.add_ssm_time_step_rank(dt_rank)
        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
        self.gguf_writer.add_expert_count(self.hparams["num_experts"])
        self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"])
        self.gguf_writer.add_file_type(self.ftype)

    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:

        # Mini-Jamba
        name = name.replace(".moe.", ".feed_forward.")
        if bid is not None:
            moe_offset = self.hparams["expert_layer_offset"]