Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic

Model Overview

• Model Architecture: LlamaForCausalLM
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Sparsity: 2:4
  • Weight quantization: FP8
  • Activation quantization: FP8
• Release Date: 06/04/2025
• Version: 1.0
• Intended Use Cases: This model is finetuned to summarize text in the style of Reddit posts.
• Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License.
• Model Developers: Red Hat (Neural Magic)

This model is a quantized version of RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4, which is fine-tuned on the trl-lib/tldr dataset. This sparse-quantized model recovers 100% of the BERTScore (0.366) obtained by the dense model RedHatAI/Llama-3.1-8B-tldr while providing up to 1.6x speedup.

Deployment

This model can be deployed efficiently using vLLM, as shown in the example below.

Run the following command to start the vLLM server:

vllm serve RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic

Once your server is started, you can query the model using the OpenAI API:

from openai import OpenAI

openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

post="""
SUBREDDIT: r/AI

TITLE: Training sparse LLMs

POST: Now you can use the llm-compressor integration to axolotl to train sparse LLMs!

It's super easy to use. See the example in https://huggingface.co/RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4.

And there's more. You can run 2:4 sparse models on vLLM and get significant speedupts on Hopper GPUs!
"""

prompt = f"Give a TL;DR of the following Reddit post.\n<|user|>{post}\nTL;DR:\n<|assistant|>\n"

completion = client.completions.create(
  model="RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic",
  prompt=prompt,
  max_tokens=256,
)
print("Completion result:", completion)

Quantization

Quantization details

This model was created by applying llm-compressor, as presented in the code snipet below.

from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

model_stub = "RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4"
model_name = model_stub.split("/")[-1]

model = AutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype="auto", device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_stub),

output_dir = f"./{model_name}-FP8-dynamic"

oneshot(
    model=model,
    recipe=recipe,
)

model.save_pretrained(output_dir, save_compressed=True, skip_sparsity_compression_stats=False)
tokenizer.save_pretrained(output_dir)

Evaluation

The model was evaluated on the test split of trl-lib/tldr using the Neural Magic fork of lm-evaluation-harness (tldr branch). One can reproduce these results by using the following command:

lm_eval --model vllm --model_args "pretrained=RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic,dtype=auto,add_bos_token=True" --batch-size auto --tasks tldr

Metric	Llama-3.1-8B-Instruct	Llama-3.1-8B-tldr	Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic (this model)
BERTScore	-0.230	0.366	0.366
ROUGE-1	0.059	0.362	0.354
ROUGE-2	0.018	0.144	0.140
ROUGE-Lsum	0.051	0.306	0.302

Inference Performance

We evaluated the inference performance of this model using the first 1,000 samples from the training set of the trl-lib/tldr dataset. Benchmarking was conducted with vLLM version 0.9.0.1 and GuideLLM version 0.2.1.

The figure below presents the mean end-to-end latency per request across varying request rates. Results are shown for this model, as well as two variants:

• Dense: Llama-3.1-8B-tldr
• Dense-quantized: Llama-3.1-8B-tldr-FP8-dynamic

Reproduction instructions

To replicate the benchmark:

1. Generate a JSON file containing the first 1,000 training samples:

from datasets import load_dataset
ds = load_dataset("trl-lib/tldr", split="train").take(1000)
ds.to_json("tldr_1000.json")

2. Start a vLLM server using your target model:

vllm serve RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic

3. Run the benchmark with GuideLLM:

GUIDELLM__OPENAI__MAX_OUTPUT_TOKENS=128 guidellm benchmark --target "http://localhost:8000" --rate-type sweep --data tldr_1000.json

The average output length is approximately 30 tokens per sample. We capped the generation at 128 tokens to reduce performance skew from rare, unusually verbose completions.