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config.json

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+{
+  "architectures": [
+    "DFlashDraftModel"
+  ],
+  "auto_map": {
+    "": "config.DFlashSpeculatorConfig"
+  },
+  "aux_hidden_state_layer_ids": null,
+  "base_model_ep_plan": null,
+  "block_size": 8,
+  "draft_vocab_size": 32000,
+  "dtype": "float32",
+  "has_no_defaults_at_init": false,
+  "num_hidden_layers": 3,
+  "speculators_config": {
+    "algorithm": "dflash",
+    "default_proposal_method": "greedy",
+    "proposal_methods": [
+      {
+        "accept_tolerance": 0.0,
+        "proposal_type": "greedy",
+        "speculative_tokens": 8,
+        "verifier_accept_k": 1
+      }
+    ],
+    "verifier": {
+      "architectures": [],
+      "name_or_path": "Qwen/Qwen3-8B"
+    }
+  },
+  "speculators_model_type": "dflash",
+  "speculators_version": "0.5.0.dev42",
+  "target_hidden_size": null,
+  "transformer_layer_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 12288,
+    "max_position_embeddings": 40960,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 3,
+    "num_key_value_heads": 8,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 10000.0,
+    "use_cache": true,
+    "vocab_size": 151936
+  },
+  "transformers_version": "4.57.6"
+}

config.py

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+from typing import Any, Literal
+
+from pydantic import Field, field_serializer, field_validator
+from transformers import AutoConfig, PretrainedConfig
+from transformers.models.qwen3.modeling_qwen3 import (
+    Qwen3Config,)
+from speculators import SpeculatorModelConfig
+
+__all__ = [
+    "DFlashSpeculatorConfig",
+]
+
+
+@SpeculatorModelConfig.register("dflash")
+class DFlashSpeculatorConfig(SpeculatorModelConfig):
+    """
+    Configuration for DFlash speculator with vocabulary mapping.
+
+    DFlash features vocabulary mapping between draft (64K) and target (128K)
+    vocabularies, enabling cross-tokenizer speculation.
+
+    :param transformer_layer_config: Configuration for the transformer decoder layer
+    :param draft_vocab_size: Size of draft model vocabulary for speculation
+    """
+
+    speculators_model_type: Literal["dflash"] = "dflash"
+    architectures: list[str] = Field(
+        default_factory=lambda: ["DFlashSpeculator"],
+        description="Model architectures that can load these weights",
+    )
+
+    transformer_layer_config: PretrainedConfig = Field(
+        default_factory=Qwen3Config,
+        description="Configuration for the transformer decoder layer",
+    )
+
+    draft_vocab_size: int = Field(
+        default=32000,
+        description="Size of draft model vocabulary for speculation",
+    )
+
+    num_hidden_layers: int = Field(
+        default=3,
+        description="Number of hidden layers in the DFlash model",
+    )
+
+    block_size: int = Field(
+        default=8,
+        description="Default size of the draft block predicted with a forward pass of the model",
+    )
+
+    target_hidden_size: int | None = Field(
+        default=None,
+        description="Hidden size of the target model (if different from draft model)",
+    )
+
+    aux_hidden_state_layer_ids: list[int] | None = Field(
+        default=None,
+        description="Layer IDs of the DFlash auxiliary hidden state layers",
+    )
+
+    @property
+    def target_vocab_size(self) -> int:
+        """Get target vocabulary size from transformer config."""
+        return self.transformer_layer_config.vocab_size
+
+    @field_serializer("transformer_layer_config")
+    def serialize_transformer_config(self, value: PretrainedConfig) -> dict:
+        """Serialize transformer config to dict."""
+        return value.to_diff_dict()
+
+    @field_validator("transformer_layer_config", mode="before")
+    @classmethod
+    def validate_transformer_config(cls, value: Any) -> PretrainedConfig:
+        """Validate and convert transformer config."""
+        if isinstance(value, dict):
+            config_class: type[PretrainedConfig] = Qwen3Config
+            if "model_type" in value:
+                config_class = AutoConfig.for_model(
+                    model_type=value["model_type"]
+                ).__class__
+            return config_class(**value)
+        return value

model.safetensors

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+size 3027718256

optimizer_state_dict.pt

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+oid sha256:0701b4ec3e9223cea605caf32f5da186fdd1ed01f6cdefb173e9f0bf1811919a
+size 2516750833

scheduler_state_dict.pt

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+oid sha256:e0430ec9e2832e07e2cb1a3b53f1419af7a1c67481b8d007f8cac7e111cdd5cc
+size 1531