from __future__ import annotations
from typing import Iterable, TYPE_CHECKING
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf
@ModelBase.register("DreamModel")
@ModelBase.example("Dream-org/Dream-v0-Instruct-7B")
class DreamModel(TextModel):
model_arch = gguf.MODEL_ARCH.DREAM
def get_vocab_base(self) -> tuple[list[str], list[int], str]:
tokens: list[str] = []
toktypes: list[int] = []
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
vocab_dict = tokenizer.get_vocab() # ty: ignore[unresolved-attribute]
vocab_size = self.hparams.get("vocab_size", len(vocab_dict))
assert max(vocab_dict.values()) < vocab_size
tokpre = self.get_vocab_base_pre(tokenizer)
reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()}
added_vocab = tokenizer.get_added_vocab() # ty: ignore[unresolved-attribute]
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.UNUSED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
# Check if it's a special token - treat special tokens as CONTROL tokens
if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder:
if tokenizer.added_tokens_decoder[i].special:
toktypes.append(gguf.TokenType.CONTROL)
else:
toktypes.append(gguf.TokenType.USER_DEFINED)
else:
# Fallback: treat all added vocab as control tokens for special tokens like <|im_start|>
toktypes.append(gguf.TokenType.CONTROL)
else:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
return tokens, toktypes, tokpre
def set_vocab(self):
try:
self._set_vocab_sentencepiece()
except FileNotFoundError:
self._set_vocab_gpt2()
def set_gguf_parameters(self):
super().set_gguf_parameters()
self._try_set_pooling_type()
# Dream models use non-causal attention for diffusion
self.gguf_writer.add_causal_attention(False)
# Add Dream-specific parameters
mask_token_id = self.hparams.get("mask_token_id")
if mask_token_id is not None:
self.gguf_writer.add_mask_token_id(mask_token_id)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# Dream model tensors should be mapped directly since it's the base model
yield from super().modify_tensors(data_torch, name, bid)