Instructions to use RedHatAI/Qwen3-32B-speculator.eagle3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Qwen3-32B-speculator.eagle3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RedHatAI/Qwen3-32B-speculator.eagle3", trust_remote_code=True)

# Load model directly
from transformers import Eagle3Speculator
model = Eagle3Speculator.from_pretrained("RedHatAI/Qwen3-32B-speculator.eagle3", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RedHatAI/Qwen3-32B-speculator.eagle3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Qwen3-32B-speculator.eagle3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen3-32B-speculator.eagle3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RedHatAI/Qwen3-32B-speculator.eagle3

SGLang

How to use RedHatAI/Qwen3-32B-speculator.eagle3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RedHatAI/Qwen3-32B-speculator.eagle3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen3-32B-speculator.eagle3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RedHatAI/Qwen3-32B-speculator.eagle3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Qwen3-32B-speculator.eagle3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RedHatAI/Qwen3-32B-speculator.eagle3 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Qwen3-32B-speculator.eagle3
```

Qwen3-32B-speculator.eagle3

Model Overview

Verifier: Qwen/Qwen3-32B
Speculative Decoding Algorithm: EAGLE-3
Model Architecture: Eagle3Speculator
Release Date: 09/17/2025
Version: 1.0
Model Developers: RedHat

This is a speculator model designed for use with Qwen/Qwen3-32B, based on the EAGLE-3 speculative decoding algorithm. It was trained using the speculators library on a combination of the Aeala/ShareGPT_Vicuna_unfiltered and the train_sft split of HuggingFaceH4/ultrachat_200k datasets. This model should be used with the Qwen/Qwen3-32B chat template, specifically through the /chat/completions endpoint.

Use with vLLM

vllm serve Qwen/Qwen3-32B \
  -tp 2 \
  --speculative-config '{
    "model": "RedHatAI/Qwen3-32B-speculator.eagle3",
    "num_speculative_tokens": 3,
    "method": "eagle3"
  }'

Evaluations

Use cases

Use Case	Dataset	Number of Samples
Coding	HumanEval	168
Math Reasoning	gsm8k	80
Text Summarization	CNN/Daily Mail	80

Acceptance lengths

Use Case	k=1	k=2	k=3	k=4	k=5	k=6	k=7
Coding	1.67	2.06	2.29	2.39	2.47	2.50	2.53
Math Reasoning	1.73	2.21	2.49	2.69	2.80	2.83	3.08
Text Summarization	1.62	1.95	2.15	2.23	2.27	2.32	2.33

Performance benchmarking (2xA100)

Details

Configuration

temperature: 0.6
top_p: 0.95
top_k: 20
repetitions: 3
time per experiment: 10min
hardware: 2xA100
vLLM version: 0.11.0
GuideLLM version: 0.3.0

Command

GUIDELLM__PREFERRED_ROUTE="chat_completions" \
guidellm benchmark \
  --target "http://localhost:8000/v1" \
  --data "RedHatAI/speculator_benchmarks" \
  --data-args '{"data_files": "HumanEval.jsonl"}' \
  --rate-type sweep \
  --max-seconds 600 \
  --output-path "Qwen3-32B-HumanEval.json" \
  --backend-args '{"extra_body": {"chat_completions": {"temperature":0.6, "top_p":0.95, "top_k":20}}}'

GuideLLM interface changed, so for compatibility with the latest version (v0.6.0), please use the following command:

GUIDELLM__PREFERRED_ROUTE="chat_completions" \
guidellm benchmark \
  --target "http://localhost:8000/v1" \
  --data "RedHatAI/speculator_benchmarks" \
  --data-args '{"data_files": "HumanEval.jsonl"}' \
  --profile sweep \
  --max-seconds 1800 \
  --output-path "my_output.json" \
  --backend-args '{"extras": {"body": {"temperature":0.6, "top_p":0.95, "top_k":20}}}'

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Safetensors

Model size

2B params

Tensor type

I64

BF16

BOOL

Collection including RedHatAI/Qwen3-32B-speculator.eagle3

Speculator Models

Collection

19 items • Updated 16 days ago • 22

Paper for RedHatAI/Qwen3-32B-speculator.eagle3

EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test

Paper • 2503.01840 • Published Mar 3, 2025 • 9