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f9599d0cc8
* vocab : fix Nemotron-3.5-Embed tokenization * Update src/llama-vocab.h Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * rename * Update src/llama-vocab.cpp Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
105 lines
4.1 KiB
Python
105 lines
4.1 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from torch import Tensor
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from .base import ModelBase, TextModel, get_model_architecture, gguf
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from .deepseek import DeepseekV2Model
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from .llama import LlamaModel
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@ModelBase.register(
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"Mistral3ForConditionalGeneration",
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"Mistral3Model",
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"Ministral3ForCausalLM",
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"Ministral3Model",
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)
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@ModelBase.example("mistralai/Mistral-Small-3.1-24B-Instruct-2503", "hf-tiny-v2/tiny-random-Ministral3ForCausalLM", "nvidia/Nemotron-3-Embed-1B-BF16", "nvidia/Nemotron-3.5-Embed-1B-BF16")
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class Mistral3Model(TextModel):
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class Ministral3Model(LlamaModel):
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model_arch = gguf.MODEL_ARCH.MISTRAL3
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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rope_params = self.rope_parameters
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if self.hparams.get("model_type") == "ministral3":
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assert rope_params, "ministral3 must have 'rope_parameters' config"
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assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'"
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self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
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self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"])
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class Mistral3EmbedNemotronModel(Ministral3Model):
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model_arch = gguf.MODEL_ARCH.MISTRAL3
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def set_vocab(self):
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super().set_vocab()
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self.gguf_writer.add_add_bos_token(False)
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self.gguf_writer.add_add_eos_token(False)
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def get_vocab_base_pre(self, tokenizer) -> str:
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if self.origin_hf_arch == "Mistral3Model":
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return "nemotron-3.5-embed"
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return super().get_vocab_base_pre(tokenizer)
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self._try_set_pooling_type()
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class Mistral4Model(DeepseekV2Model):
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model_arch = gguf.MODEL_ARCH.MISTRAL4
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skip_mtp = False # model contains no MTP layers, so no need to skip
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merge_expert = False # experts are already stacked as 3D
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def modify_tensors(self, data_torch, name, bid):
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if name.endswith(".down_proj") or name.endswith(".gate_up_proj"):
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name = name + ".weight"
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yield from super().modify_tensors(data_torch, name, bid)
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model_arch = gguf.MODEL_ARCH.MISTRAL3 # unused
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impl: TextModel
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def __init__(self, dir_model, *args, **kwargs):
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hparams = ModelBase.load_hparams(dir_model, is_mistral_format=False)
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hf_arch = get_model_architecture(hparams, self.model_type)
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if hf_arch in ("Ministral3Model", "Mistral3Model"):
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text_config = hparams.get("text_config", hparams)
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if text_config.get("is_causal") is not False:
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raise NotImplementedError(f"Expected a Nemotron Embed model for {hf_arch}, but is_causal is not False")
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super().__init__(dir_model, *args, **kwargs)
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if self.hparams.get("model_type") == "mistral4":
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self.impl = Mistral3Model.Mistral4Model(dir_model, *args, **kwargs)
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elif self.hf_arch in ("Ministral3Model", "Mistral3Model"):
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self.impl = Mistral3Model.Mistral3EmbedNemotronModel(dir_model, *args, **kwargs)
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else:
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self.impl = Mistral3Model.Ministral3Model(dir_model, *args, **kwargs)
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def set_vocab(self):
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self.impl.set_vocab()
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def set_gguf_parameters(self):
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self.impl.set_gguf_parameters()
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
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yield from self.impl.modify_tensors(data_torch, name, bid)
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def prepare_tensors(self):
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self.impl.prepare_tensors()
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def write_vocab(self):
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self.impl.write_vocab()
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def write(self):
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self.impl.write()
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@ModelBase.register("VoxtralRealtimeForConditionalGeneration")
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@ModelBase.example("mistralai/Voxtral-Mini-4B-Realtime-2602")
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class VoxtralRealtimeModel(LlamaModel):
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model_arch = gguf.MODEL_ARCH.MISTRAL3
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_realtime_delay_tokens(self.hparams["default_num_delay_tokens"])
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