from __future__ import annotations
import json
from typing import Iterable, TYPE_CHECKING
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf, logger
@ModelBase.register("PanguEmbeddedForCausalLM")
@ModelBase.example("FreedomIntelligence/openPangu-Embedded-7B-V1.1")
class PanguEmbeddedModel(TextModel):
model_arch = gguf.MODEL_ARCH.PANGU_EMBED
def set_vocab(self):
self._set_vocab_sentencepiece()
tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
if tokenizer_config_file.is_file():
with open(tokenizer_config_file, "r", encoding="utf-8") as f:
tokenizer_config_json = json.load(f)
if "add_prefix_space" in tokenizer_config_json:
self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
# PanguEmbedded's hparam loaded from config.json without head_dim
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
if hparams.get("head_dim") is None:
self.gguf_writer.add_key_length(rope_dim)
self.gguf_writer.add_value_length(rope_dim)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name == "lm_head.weight":
if self.hparams.get("tie_word_embeddings", False):
logger.info("Skipping tied output layer 'lm_head.weight'")
return
yield from super().modify_tensors(data_torch, name, bid)