docs : Eagle3 qwen3 draft model support (#24977)

* eagle3: accept Eagle3LlamaForCausalLM draft checkpoints

* docs: add eagle3 speculative decoding section

* docs: address eagle3 review comments

* docs: add more angelslim eagle3 models

* docs: add gpt-oss eagle3 models and link to pr 18039

diff --git a/conversion/__init__.py b/conversion/__init__.py
index a587b1c37e275c813834685285f5de9232183073..5aad203e53c3f458aca5075899841597cd0a4f67 100644 (file)
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -136,6 +136,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "LlamaModel": "llama",
     "Eagle3DraftModel": "llama",
     "Eagle3Speculator": "llama",
+    "Eagle3LlamaForCausalLM": "llama",
     "LlamaForCausalLMEagle3": "llama",
     "LlavaForConditionalGeneration": "llama",
     "LlavaStableLMEpochForCausalLM": "stablelm",

diff --git a/conversion/llama.py b/conversion/llama.py
index a0d39472ebb1dad155af65ef6fea875de83be088..b43cc994aa3a6d663ba43c02137adb9bcbc1d7d2 100644 (file)
--- a/conversion/llama.py
+++ b/conversion/llama.py
@@ -23,6 +23,7 @@ from .base import ModelBase, TextModel, gguf, logger
     "LlavaForConditionalGeneration",
     "VoxtralForConditionalGeneration",
     "LlamaForCausalLMEagle3",
+    "Eagle3LlamaForCausalLM",
     "Eagle3Speculator",
     "Eagle3DraftModel",
     "IQuestCoderForCausalLM",

diff --git a/docs/speculative.md b/docs/speculative.md
index 43d1818589121dacc4ba0af81f99abf21238a217..8f91256c4a4d6b6c8238cdeb32cce66346458323 100644 (file)
--- a/docs/speculative.md
+++ b/docs/speculative.md
@@ -13,6 +13,45 @@ The `llama-server` application supports several implementations of speculative d
 A much smaller model (called the _draft model_) generates drafts.
 A draft model is the most used approach in speculative decoding.
 
+### EAGLE-3 (`draft-eagle3`)
+
+EAGLE-3 uses a small draft model that reads the target model's hidden states to predict the next tokens, so it
+reaches higher acceptance than a standalone draft model of the same size. The draft is a one-layer transformer
+trained for a specific target model; it shares the target model's tokenizer and, optionally, uses a reduced draft
+vocabulary with its own `lm_head`, which is mapped back using a `d2t` table.
+
+Convert the EAGLE-3 checkpoint with `--target-model-dir` so it inherits the target's tokenizer and the layer
+indices to read. Both the SpecForge `LlamaForCausalLMEagle3` and the vLLM/AngelSlim `Eagle3LlamaForCausalLM`
+checkpoint formats are supported (for example [`AngelSlim/Qwen3-4B_eagle3`](https://huggingface.co/AngelSlim/Qwen3-4B_eagle3)
+for `Qwen/Qwen3-4B`):
+
+```bash
+python convert_hf_to_gguf.py AngelSlim/Qwen3-4B_eagle3 \
+    --target-model-dir Qwen/Qwen3-4B --outtype bf16 --outfile Qwen3-4B-eagle3.gguf
+
+llama-server -m Qwen3-4B.gguf -md Qwen3-4B-eagle3.gguf --spec-type draft-eagle3
+```
+
+Supported EAGLE-3 draft models include:
+
+- [yuhuili/EAGLE3-LLaMA3.1-Instruct-8B](https://huggingface.co/yuhuili/EAGLE3-LLaMA3.1-Instruct-8B)
+- [yuhuili/EAGLE3-LLaMA3.3-Instruct-70B](https://huggingface.co/yuhuili/EAGLE3-LLaMA3.3-Instruct-70B)
+- [RedHatAI/gemma-4-31B-it-speculator.eagle3](https://huggingface.co/RedHatAI/gemma-4-31B-it-speculator.eagle3)
+- [RedHatAI/gemma-4-26B-A4B-it-speculator.eagle3](https://huggingface.co/RedHatAI/gemma-4-26B-A4B-it-speculator.eagle3)
+- [Tengyunw/qwen3_8b_eagle3](https://huggingface.co/Tengyunw/qwen3_8b_eagle3)
+- [Tengyunw/qwen3_30b_moe_eagle3](https://huggingface.co/Tengyunw/qwen3_30b_moe_eagle3)
+- [AngelSlim/Qwen3-1.7B_eagle3](https://huggingface.co/AngelSlim/Qwen3-1.7B_eagle3)
+- [AngelSlim/Qwen3-4B_eagle3](https://huggingface.co/AngelSlim/Qwen3-4B_eagle3)
+- [AngelSlim/Qwen3-8B_eagle3](https://huggingface.co/AngelSlim/Qwen3-8B_eagle3)
+- [AngelSlim/Qwen3-14B_eagle3](https://huggingface.co/AngelSlim/Qwen3-14B_eagle3)
+- [AngelSlim/Qwen3-32B_eagle3](https://huggingface.co/AngelSlim/Qwen3-32B_eagle3)
+- [AngelSlim/Qwen3-a3B_eagle3](https://huggingface.co/AngelSlim/Qwen3-a3B_eagle3)
+- [RedHatAI/gpt-oss-20b-speculator.eagle3](https://huggingface.co/RedHatAI/gpt-oss-20b-speculator.eagle3)
+- [lmsys/EAGLE3-gpt-oss-120b-bf16](https://huggingface.co/lmsys/EAGLE3-gpt-oss-120b-bf16)
+- [nvidia/gpt-oss-120b-Eagle3-long-context](https://huggingface.co/nvidia/gpt-oss-120b-Eagle3-long-context)
+
+For the full and up-to-date list of supported models, see #18039.
+
 ### n-gram Cache (`ngram-cache`)
 
 An n-gram is a sequence of n tokens. The n-gram cache implementation maintains statistics about short n-gram sequences.
@@ -108,7 +147,7 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
 ### General Speculative Parameters
 
 ```
---spec-type [none|draft-simple|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
+--spec-type [none|draft-simple|draft-eagle3|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
                                         comma-separated list of types of speculative decoding to use
                                         (default: none)
                                         (env: LLAMA_ARG_SPEC_TYPE)
@@ -247,6 +286,7 @@ Specifies a comma-separated list of speculative decoding types to use.
 |------|-------------|
 | `none` | No speculative decoding (default) |
 | `draft-simple` | Use a simple draft model for speculation |
+| `draft-eagle3` | Use an EAGLE-3 draft model that reads the target's hidden states |
 | `draft-mtp` | Use Multi Token Prediction (MTP) heads from the main model |
 | `ngram-cache` | Use n-gram cache lookup |
 | `ngram-simple` | Use simple n-gram pattern matching |