Upload Pearl EAGLE3.1 R2C best head

README.md

@@ -0,0 +1,32 @@
+---
+license: apache-2.0
+tags:
+- eagle3
+- speculative-decoding
+- pearl
+- llama-3.1
+---
+
+# Pearl EAGLE3.1 R2C best head (2026-06-19)
+
+Selected head: **R2C_t7_g115**.
+
+Selection rule: best overall 8×H100 serving throughput, ranked by aggregate best-K req/s across per-GPU concurrency C=1/4/16 and prompt-length buckets. K4/K5 vs K3 was diagnostic only; the selected checkpoint is the one with the best overall serving result.
+
+Final rank by aggregate best-K req/s:
+
+1. R2C — 11.0288
+2. D — 11.0108
+3. I75 — 11.0029
+4. R4B — 10.9846
+5. R3B — 10.9305
+
+Full run artifacts, training states, logs, benchmark outputs, and selection manifests were archived to:
+
+`s3://pearl-eagle31-artifacts-507286591552-us-east-2/eagle31-final-r2c-20260619T1318Z`
+
+The PR branch was also updated with the multi-replica benchmark used for final evaluation:
+
+`feat/real-prompt-eagle-mining-bench` @ `85861376 feat: add multi-replica EAGLE benchmark`
+
+This HF repo contains the selected head files needed for serving/eval (`model.safetensors`, `config.json`, `config.py`, `val_metrics.json`) plus selection metadata. Optimizer/scheduler training state is in the S3 archive, not this model repo.

config.json

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+{
+  "architectures": [
+    "Eagle3DraftModel"
+  ],
+  "auto_map": {
+    "": "config.Eagle3SpeculatorConfig"
+  },
+  "draft_vocab_size": 32000,
+  "dtype": "bfloat16",
+  "eagle_aux_hidden_state_layer_ids": [
+    2,
+    16,
+    29
+  ],
+  "embed_requires_grad": false,
+  "norm_before_fc": true,
+  "norm_before_residual": true,
+  "speculators_config": {
+    "algorithm": "eagle3",
+    "default_proposal_method": "greedy",
+    "proposal_methods": [
+      {
+        "accept_tolerance": 0.0,
+        "proposal_type": "greedy",
+        "speculative_tokens": 3,
+        "verifier_accept_k": 1
+      }
+    ],
+    "verifier": {
+      "architectures": [],
+      "name_or_path": "pearl-ai/Llama-3.1-8B-Instruct-pearl"
+    }
+  },
+  "speculators_model_type": "eagle3",
+  "speculators_version": "0.6.0.dev0",
+  "target_hidden_size": null,
+  "tie_word_embeddings": false,
+  "transformer_layer_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "layer_types": [
+      "full_attention"
+    ],
+    "max_position_embeddings": 131072,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 1,
+    "num_key_value_heads": 8,
+    "pad_token_id": null,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+      "factor": 8.0,
+      "high_freq_factor": 4.0,
+      "low_freq_factor": 1.0,
+      "original_max_position_embeddings": 8192,
+      "rope_theta": 500000.0,
+      "rope_type": "llama3"
+    },
+    "sliding_window": 2048,
+    "tie_word_embeddings": false,
+    "use_cache": true,
+    "vocab_size": 128256
+  },
+  "transformers_version": "5.10.4"
+}

config.py

@@ -0,0 +1,93 @@
+from typing import Any, Literal
+
+from pydantic import Field, field_serializer, field_validator
+from transformers import AutoConfig, PretrainedConfig
+from transformers.models.llama.configuration_llama import LlamaConfig
+
+from speculators import SpeculatorModelConfig
+
+__all__ = [
+    "Eagle3SpeculatorConfig",
+]
+
+
+@SpeculatorModelConfig.register("eagle3")
+class Eagle3SpeculatorConfig(SpeculatorModelConfig):
+    """
+    Configuration for EAGLE-3 speculator with vocabulary mapping.
+
+    EAGLE-3 features vocabulary mapping between draft (32K) 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
+    :param norm_before_residual: Apply hidden_norm before storing residual
+    """
+
+    speculators_model_type: Literal["eagle3"] = "eagle3"
+    architectures: list[str] = Field(
+        default_factory=lambda: ["Eagle3Speculator"],
+        description="Model architectures that can load these weights",
+    )
+
+    transformer_layer_config: PretrainedConfig = Field(
+        default_factory=LlamaConfig,
+        description="Configuration for the transformer decoder layer",
+    )
+
+    draft_vocab_size: int = Field(
+        default=32000,
+        description="Size of draft model vocabulary for speculation",
+    )
+
+    norm_before_residual: bool = Field(
+        default=False,
+        description="Apply hidden_norm before storing residual",
+    )
+
+    target_hidden_size: int | None = Field(
+        default=None,
+        description="Hidden size of the target model (if different from draft model)",
+    )
+
+    eagle_aux_hidden_state_layer_ids: list[int] | None = Field(
+        default=None,
+        description="Layer IDs of the Eagle auxiliary hidden state layers",
+    )
+
+    norm_before_fc: bool = Field(
+        default=False,
+        description=(
+            "If True, vLLM will add and apply RMSNorm before the fc layer when loading "
+            "this draft model (e.g. for gpt-oss draft checkpoints). Set in config when "
+            "converting or saving gpt-oss draft models."
+        ),
+    )
+
+    embed_requires_grad: bool = Field(
+        default=False,
+        description="Whether embedding layer weights require gradients during training",
+    )
+
+    @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] = LlamaConfig
+            if "model_type" in value:
+                config_class = AutoConfig.for_model(
+                    model_type=value["model_type"]
+                ).__class__
+            return config_class(**value)
+        return value
+
+    @property
+    def target_vocab_size(self) -> int:
+        """Get target vocabulary size from transformer config."""
+        return self.transformer_layer_config.vocab_size

final_best_overall_selection.json

@@ -0,0 +1,1270 @@
+{
+  "rank": [
+    "R2C",
+    "D",
+    "I75",
+    "R4B",
+    "R3B"
+  ],
+  "selected": "R2C",
+  "selected_checkpoint": "/opt/dlami/nvme/pearl-work/runs/eagle31_from_D_R2_20260619T101914Z/checkpoints/R2C_t7_g115/0",
+  "score": {
+    "D": {
+      "checkpoint": "/opt/dlami/nvme/pearl-work/runs/eagle31_synth_D_20260618T230500Z/checkpoints/D_lr3e6_ttt5/0",
+      "bestK_mean_req": 11.01077046868981,
+      "bestK_median_req": 6.24893154018202,
+      "all_modes_mean_req": 10.639310299572864,
+      "all_modes_median_req": 6.208932599213651,
+      "bestK_wins": 4,
+      "bestK_n": 21,
+      "same_mode_wins": 13,
+      "same_mode_n": 63,
+      "best_rows": [
+        {
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+    "R2C": {
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+      "all_modes_mean_req": 10.646122555261488,
+      "all_modes_median_req": 6.268194489166301,
+      "bestK_wins": 5,
+      "bestK_n": 21,
+      "same_mode_wins": 13,
+      "same_mode_n": 63,
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+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:96333fe40686de700d3d1db4885ffff749d65f846adc8dd0195dfbe0cc9cb449
+size 1900463040

selected_head_meta.json

@@ -0,0 +1,10 @@
+{
+  "selected": "R2C",
+  "source_checkpoint": "/opt/dlami/nvme/pearl-work/runs/eagle31_from_D_R2_20260619T101914Z/checkpoints/R2C_t7_g115/0",
+  "selection_rule": "best overall 8xH100 serving throughput by aggregate best-K req/s across per-GPU C=1/4/16 and prompt buckets",
+  "final_rank": ["R2C", "D", "I75", "R4B", "R3B"],
+  "bestK_mean_req_s": {"R2C": 11.0288, "D": 11.0108, "I75": 11.0029, "R4B": 10.9846, "R3B": 10.9305},
+  "s3_artifact_prefix": "s3://pearl-eagle31-artifacts-507286591552-us-east-2/eagle31-final-r2c-20260619T1318Z",
+  "pr_branch": "feat/real-prompt-eagle-mining-bench",
+  "pr_commit": "85861376 feat: add multi-replica EAGLE benchmark"
+}

val_metrics.json

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