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dflash: refactor draft model conversion (#25110)

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* dflash: refactor draft model conversion

* apply fix for eagle3 convert
This commit is contained in:
Ruixiang Wang
2026-06-28 20:31:48 +02:00
committed by GitHub
parent c818263f2a
commit fa72bc6826
5 changed files with 31 additions and 17 deletions
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conversion/qwen.py
+10 -14
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@@ -643,21 +643,21 @@ class DFlashModel(Qwen3Model):
super().set_vocab()
self.dir_model = original_dir
mask_token_id = self.hparams.get("dflash_config", {}).get("mask_token_id")
if mask_token_id is not None:
self.gguf_writer.add_mask_token_id(mask_token_id)
def set_gguf_parameters(self):
super().set_gguf_parameters()
block_size = self.hparams.get("block_size", 16)
self.gguf_writer.add_uint32(f"{self.gguf_writer.arch}.block_size", block_size)
self.gguf_writer.add_block_size(block_size)
dflash_config = self.hparams.get("dflash_config", {})
target_layer_ids = dflash_config.get("target_layer_ids", [])
if target_layer_ids:
extract_layer_ids = [i + 1 for i in target_layer_ids]
self.gguf_writer.add_array(f"{self.gguf_writer.arch}.target_layers", extract_layer_ids)
mask_token_id = dflash_config.get("mask_token_id", None)
if mask_token_id is not None:
self.gguf_writer.add_mask_token_id(mask_token_id)
self.gguf_writer.add_target_layers(extract_layer_ids)
use_sliding_window = self.hparams.get("use_sliding_window", False)
sliding_window = self.hparams.get("sliding_window")
@@ -667,13 +667,9 @@ class DFlashModel(Qwen3Model):
self.gguf_writer.add_sliding_window(sliding_window)
self.gguf_writer.add_sliding_window_pattern(is_swa)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name == "fc.weight":
yield (name, data_torch)
return
if name == "hidden_norm.weight":
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ENC_OUTPUT_NORM), data_torch)
return
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
if not name.startswith("model."):
name = "model." + name
yield from super().modify_tensors(data_torch, name, bid)
return super().filter_tensors((name, gen))