Llama-3.1-8B-Instruct-KV-Cache-FP8

Llama-3.1-8B-Instruct-KV-Cache-FP8

Model Overview

• Model Architecture: nm-testing/Llama-3.1-8B-Instruct-KV-Cache-FP8
  • Input: Text
  • Output: Text
• Release Date:
• Version: 1.0
• Model Developers:: Red Hat

FP8 KV Cache Quantization of meta-llama/Llama-3.1-8B-Instruct.

Model Optimizations

This model was obtained by quantizing the KV Cache of weights and activations of meta-llama/Llama-3.1-8B-Instruct to FP8 data type.

Deployment

Use with vLLM

1. Initialize vLLM server:

vllm serve RedHatAI/Llama-3.1-8B-Instruct-KV-Cache-FP8 --tensor_parallel_size 1

1. Send requests to the server:

from openai import OpenAI

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://<your-server-host>:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

model = "RedHatAI/Llama-3.1-8B-Instruct-KV-Cache-FP8"

messages = [
    {"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]


outputs = client.chat.completions.create(
    model=model,
    messages=messages,
)

generated_text = outputs.choices[0].message.content
print(generated_text)

<!-- ## Creation This model was quantized using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as shown below. <details> <summary>Creation details</summary> ```python from transformers import AutoProcessor, Qwen3ForCausalLM from llmcompressor import oneshot from llmcompressor.modeling import replace_modules_for_calibration from llmcompressor.modifiers.quantization import QuantizationModifier MODEL_ID = "Qwen/Qwen3-8B" # Load model. model = Qwen3ForCausalLM.from_pretrained(MODEL_ID, dtype="auto") processor = AutoProcessor.from_pretrained(MODEL_ID) model = replace_modules_for_calibration(model) # Configure the quantization algorithm and scheme. # In this case, we: # * quantize the weights to fp8 with per-block quantization # * quantize the activations to fp8 with dynamic token activations recipe = QuantizationModifier( targets="Linear", scheme="FP8_BLOCK", ignore=["lm_head"], ) # Apply quantization. oneshot(model=model, recipe=recipe) # Save to disk in compressed-tensors format. SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-block" model.save_pretrained(SAVE_DIR) processor.save_pretrained(SAVE_DIR) ``` </details> -->

Evaluation

The model was evaluated on the RULER and long-context benchmarks (LongBench), using lm-evaluation-harness. vLLM was used for all evaluations.

Accuracy

<table> <thead> <tr> <th>Category</th> <th>Metric</th> <th>meta-llama/Llama-3.1-8B-Instruct</th> <th>nm-testing/Llama-3.1-8B-Instruct-KV-Cache-FP8</th> <th>Recovery (%)</th> </tr> </thead> <tbody> <tr> <td rowspan="1"><b>LongBench V1</b></td> <td>Task 1</td> <td>abc</td> <td>ijk</td> <td>xyz</td> </tr> <tr> <td rowspan="6"><b>NIAH</b></td> <td>niah_single_1</td> <td>abc</td> <td>ijk</td> <td>xyz</td> </tr> <tr> <td>niah_single_2</td> <td>abc</td> <td>ijk</td> <td>xyz</td> </tr> <tr> <td>niah_single_3</td> <td>abc</td> <td>ijk</td> <td>xyz</td> </tr> <tr> <td>niah_multikey_1</td> <td>abc</td> <td>ijk</td> <td>xyz</td> </tr> <tr> <td>niah_multikey_2</td> <td>abc</td> <td>ijk</td> <td>xyz</td> </tr> <tr> <td>niah_multikey_3</td> <td>abc</td> <td>ijk</td> <td>xyz</td> </tr> <tr> <td><b>Average Score</b></td> <td><b>abc</b></td> <td><b>ijk</b></td> <td><b>xyz</b></td> </tr> </tbody> </table>