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RedHatAI/
Llama-3.1-8B-Instruct-speculator.eagle3

Name: Llama-3.1-8B-Instruct-speculator.eagle3
Availability: InStock

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Llama-3.1-8B-Instruct-speculator.eagle3

Model Overview

Verifier: meta-llama/Llama-3.1-8B-Instruct
Speculative Decoding Algorithm: EAGLE-3
Model Architecture: Eagle3Speculator
Release Date: 07/27/2025
Version: 1.0
Model Developers: RedHat

This is a speculator model designed for use with meta-llama/Llama-3.1-8B-Instruct, 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 HuggingFaceH4/ultrachat_200k datasets. This model should be used with the meta-llama/Llama-3.1-8B-Instruct chat template, specifically through the /chat/completions endpoint.

Use with vLLM

bash

vllm serve meta-llama/Llama-3.1-8B-Instruct \
  -tp 1 \
  --speculative-config '{
    "model": "RedHatAI/Llama-3.1-8B-Instruct-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	1.84	2.50	3.02	3.36	3.61	3.83	3.89
Math Reasoning	1.80	2.40	2.83	3.13	3.27	3.40	3.83
Text Summarization	1.70	2.19	2.50	2.78	2.77	2.98	2.99

Performance benchmarking (1xA100)

Details

Configuration

temperature: 0.6
top_p: 0.9
repetitions: 5
time per experiment: 3min
hardware: 1xA100
vLLM version: 0.11.0
GuideLLM version: 0.3.0

Command

bash

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 180 \
  --output-path "Llama-3.1-8B-Instruct-HumanEval.json" \
  --backend-args '{"extra_body": {"chat_completions": {"temperature":0.0}}}'

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

bash

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}}}'